Introduction

Statistical analysis of plasma carotenoids, plasma cytokines and immune cells from randomized cross-over USDA inflammation clinical trial. Subjects consumed both low lycopene tomato (yellow) and high lycopene tomato-soy juices (red) for 4 weeks each.

Crossover clinical trial design supplementing individuals with obesity 360 mL of a low carotenoid tomato juice or a high lycopene tomato-soy juice daily. Daily serving of low carotenoid tomato juice consisted of ~1.5mg lycopene/day while high lycopene tomato-soy juice intervention consisted of 54 mg lycopene/day in addition to 210 mg total soy isoflavones/day.

Crossover clinical trial design supplementing individuals with obesity 360 mL of a low carotenoid tomato juice or a high lycopene tomato-soy juice daily. Daily serving of low carotenoid tomato juice consisted of ~1.5mg lycopene/day while high lycopene tomato-soy juice intervention consisted of 54 mg lycopene/day in addition to 210 mg total soy isoflavones/day.

Load libraries

library(tidyverse) # data wrangling
library(readxl) # read in excel files
library(janitor) # clean up names in dataset
library(corrr) # finding correlations
library(rstatix) # stats
library(lme4) # mixed linear modeling
library(knitr) # aesthetic table viewing
library(lmerTest) # add pvalue column to lmer models
library(purrr) # create functions
library(broom.mixed) # generate tidy data frames for lmer results
library(MuMIn) # lmer model testing using AICc
library(kableExtra)
library(ggthemes)
library(ggtext)
library(ggpubr)

Read in data

# load data
meta_table <- read_excel("CompiledData_Results_Meta.xlsx",
                         sheet = "metadata_corrected_withsequence")

# clean up variable names 
meta_table <- clean_names(meta_table)

str(meta_table)
## tibble [60 × 89] (S3: tbl_df/tbl/data.frame)
##  $ patient_id                          : num [1:60] 6101 6102 6103 6104 6105 ...
##  $ sex                                 : chr [1:60] "F" "M" "F" "M" ...
##  $ age_at_enrollment                   : num [1:60] 58 65 60 54 40 57 68 75 36 46 ...
##  $ bmi_at_enrollment                   : num [1:60] 31.1 36.8 30.3 33.1 30.4 ...
##  $ intervention                        : chr [1:60] "Red" "Red" "Red" "Yellow" ...
##  $ sequence                            : chr [1:60] "R_Y" "R_Y" "R_Y" "Y_R" ...
##  $ intervention_week                   : num [1:60] 2 2 2 2 2 2 2 2 2 2 ...
##  $ pre_post                            : chr [1:60] "pre" "pre" "pre" "pre" ...
##  $ period                              : chr [1:60] "B1" "B1" "B1" "B1" ...
##  $ if_ng                               : num [1:60] 1.16 11.74 0.93 0.63 1.88 ...
##  $ il_1b                               : num [1:60] 15.26 28.75 8.62 11.04 22.5 ...
##  $ il_2                                : chr [1:60] "0.98" "9.32" "0.55000000000000004" "0.65" ...
##  $ il_6                                : num [1:60] 2.18 5.83 2.53 1.29 2.25 ...
##  $ il_8                                : num [1:60] 2.86 4.35 3.93 1.93 4.14 3.05 1.35 2.01 4.43 2.86 ...
##  $ il_10                               : chr [1:60] "0.05" "0.71" "1.89" "7.17" ...
##  $ il_12p70                            : num [1:60] 2.86 60.93 5.46 2.79 4.06 ...
##  $ mcp_1                               : num [1:60] 51.9 189.3 224.9 244.7 167.9 ...
##  $ tn_fa                               : num [1:60] 14.9 92.3 26.5 19.6 49.6 ...
##  $ il_13                               : chr [1:60] "9.64" "92.04" "21.61" "23.17" ...
##  $ il_5                                : num [1:60] 3 13.04 2.74 5.45 5.38 ...
##  $ il_1ra                              : num [1:60] 3.52 11.36 3.71 3.38 6.08 ...
##  $ il_12p40                            : num [1:60] 51.5 139.4 81 78.7 175.9 ...
##  $ gm_csf                              : num [1:60] 10.9 195.7 17.9 27.4 72.4 ...
##  $ il_4                                : chr [1:60] "NA" "8.8000000000000007" "1.6" "4.49" ...
##  $ x01_cd45_cd66b_lymph_dc_mono        : num [1:60] 99.3 95.6 99.7 99.7 96.4 ...
##  $ x02_cd45_cd66b_grans                : num [1:60] 0.682 0.33 0.305 0.285 0.696 ...
##  $ cd45_cd14                           : num [1:60] 46.9 71 69.8 55.8 42.4 ...
##  $ cd45_cd20                           : num [1:60] 44.2 59.9 60.6 54 31.9 ...
##  $ x03_cd3_cd45_cd3_t_cells            : num [1:60] 25.7 43.7 54.5 14.2 14.3 ...
##  $ x04_tc_rgd_cd3_ab_t_cells           : num [1:60] 22 42.6 53.1 11.2 13.6 ...
##  $ x05_cd4_cd8_cd8_t_cells             : num [1:60] 11.96 3.84 13.23 6.5 5.72 ...
##  $ ccr7_cd8                            : num [1:60] 4.385 1.54 4.186 0.962 1.882 ...
##  $ ccr7_cd8_2                          : num [1:60] 7.56 2.28 9.04 5.52 3.83 ...
##  $ x06_cd45ro_cd45ra_naive_cd8         : num [1:60] 3.692 0.233 3.753 0.652 1.667 ...
##  $ x07_cd46ro_cd45ra_cm_cd8            : num [1:60] 0.506 0.955 0.263 0.196 0.146 ...
##  $ x08_cd45ro_cd45ra_em_cd8            : num [1:60] 6.555 0.395 7.505 4.528 1.807 ...
##  $ x09_cd45r0_cd45ra_te_cd8            : num [1:60] 0.826 1.322 1.2 0.775 1.661 ...
##  $ x10_cd38_hladr_activated_cd8        : num [1:60] 0.2543 0.0823 0.1523 0.1347 0.0742 ...
##  $ x11_cd4_cd8_cd4_t_cells             : num [1:60] 9.14 37.12 37.51 3.64 6.98 ...
##  $ ccr7_cd4                            : num [1:60] 7.98 33.92 32.87 2.49 5.3 ...
##  $ ccr7_cd4_2                          : num [1:60] 1.16 3.19 4.64 1.15 1.68 ...
##  $ x12_cd45ro_cd45ra_naive_cd4         : num [1:60] 3.163 3.77 17.23 0.693 1.731 ...
##  $ x13_cd45ro_cd45ra_cm_cd4            : num [1:60] 3.51 22.39 8.5 1.25 2.63 ...
##  $ x14_cd45ro_cd45ra                   : num [1:60] 1.053 2.964 3.919 0.893 1.488 ...
##  $ x15_cd45ro_cd45ra_te_cd4            : num [1:60] 0.0257 0.0664 0.5457 0.1154 0.092 ...
##  $ x16_cd38_hladr_activated_cd4        : num [1:60] 0.321 0.698 0.384 0.123 0.14 ...
##  $ cd45_cxcr5_th                       : num [1:60] 8.291 28.987 0.133 2.95 6.456 ...
##  $ x17_cd25_cd127_tregs                : num [1:60] 0.348 0.545 0.438 0.131 0.193 ...
##  $ x18_ccr4_cd4_total_ccr4_treg        : num [1:60] 0.317 0.494 0.316 0.123 0.18 ...
##  $ x19_cd45ra_cd45ro_ccr4_treg_naive   : num [1:60] 0.00973 0.03494 0.00676 0.00955 0.00744 ...
##  $ x21_cd45ra_cd45ro_ccr4_treg_memory  : num [1:60] 0.3017 0.4366 0.2905 0.0949 0.166 ...
##  $ x20_hladr_total_ccr4_treg_activated : num [1:60] 0.2867 0.2547 0.1017 0.0838 0.1119 ...
##  $ x22_cxcr3_ccr6_th1                  : num [1:60] 0.7981 2.0038 0.0209 0.4805 0.8362 ...
##  $ x23_cxcr3_ccr6_th2                  : num [1:60] 5.6202 13.4081 0.0747 1.2236 4.3881 ...
##  $ x24_cxcr3_ccr6_th17                 : num [1:60] 1.1118 9.5315 0.0153 0.6605 0.3389 ...
##  $ cd16_cd161                          : num [1:60] 27.1 46.9 55.1 15 23.5 ...
##  $ x25_cd19_cd3_b_cells                : num [1:60] 2.53 8.22 7.86 1.62 9.48 ...
##  $ cd20                                : num [1:60] 0.115 0.111 0.2 0.147 0.102 ...
##  $ cd20_2                              : num [1:60] 2.41 8.09 7.64 1.47 9.38 ...
##  $ x26_cd27_ig_d_naive_b_cells         : num [1:60] 1.19 6.99 4.62 1.29 6.69 ...
##  $ x27_cd27_ig_d_memory_b_cells        : num [1:60] 0.697 0.529 2.22 0.101 0.878 ...
##  $ x28_cd27_ig_d_memory_resting_b_cells: num [1:60] 0.1083 0.2849 0.304 0.0499 1.4241 ...
##  $ x30_cd27_cd38_plasmablasts          : num [1:60] 0.0634 0.0277 0.1582 0.0378 0.062 ...
##  $ cd19_cd3                            : num [1:60] 66.8 35.6 30.2 79.9 65.8 ...
##  $ x31_cd14_monocytes                  : num [1:60] 47.7 19.5 23.2 41.8 48.7 ...
##  $ x32_cd16_non_classical_mono         : num [1:60] 6.24 3.29 3.07 7.15 3.4 ...
##  $ x33_cd16_classical_mono             : num [1:60] 38.5 16.1 19.1 33.5 45.1 ...
##  $ x34_hladr_cd56                      : num [1:60] 43.8 16.7 20.3 33.9 41.1 ...
##  $ cd16_cd123                          : num [1:60] 1.142 0.272 0.657 0.645 0.613 ...
##  $ x35_cd16_cd123_cd11c_p_dc           : num [1:60] 0.709 0.248 0.594 0.37 0.495 ...
##  $ cd16_cd123_2                        : num [1:60] 33.1 10.5 15.1 25.6 37.3 ...
##  $ x36_cd16_cd123_cd11c_m_dc           : num [1:60] 27.7 10.4 14.7 23.6 37.1 ...
##  $ cd123                               : num [1:60] 65.7 35.1 29.5 73.5 65.1 ...
##  $ total_dc                            : num [1:60] 29.5 10.9 15.9 34 38.2 ...
##  $ x37_cd56_cd161_cd123_nk_cells       : num [1:60] 13.01 9.43 2.44 5.33 14.75 ...
##  $ x38_cd16_nk_cells                   : num [1:60] 1.601 1.552 0.874 28.656 5.754 ...
##  $ cd16_nk_cells                       : num [1:60] 11.41 7.85 1.56 41.75 8.9 ...
##  $ cd14_cd11b                          : num [1:60] 46.4 17.3 20.5 35 48.7 ...
##  $ x40_cd14_mdsc_mono                  : num [1:60] 28.983 7.399 9.73 0.215 39.928 ...
##  $ cd66b_cd11b                         : num [1:60] 0.314 0.251 0.265 0.195 0.532 ...
##  $ x41_cd66b_mdsc_grans                : num [1:60] 0.2057 0.0724 0.0985 NA 0.4165 ...
##  $ lutein                              : num [1:60] 735 120 556 419 240 ...
##  $ zeaxanthin                          : num [1:60] 94.6 140.1 99 81.6 53.7 ...
##  $ b_cryptoxanthin                     : num [1:60] 314 286 389 230 570 ...
##  $ a_carotene                          : num [1:60] 195.7 101.8 181.2 112.6 37.1 ...
##  $ b_carotene                          : num [1:60] 927 309 434 536 121 ...
##  $ other_cis_lyc                       : num [1:60] 151 276 161 165 273 ...
##  $ all_trans_lyc                       : num [1:60] 115 259 185 175 336 ...
##  $ x5_cis_lyc                          : num [1:60] 132 262 166 149 235 ...

Wrangle

# convert variables that should be factors to factors
meta_table <- meta_table %>%
  mutate(across(.cols = c("patient_id", "period", 
                          "intervention", "intervention_week", 
                          "pre_post", "sex", "sequence"),
                .fns = as.factor))


# some stuff came in as characters but should be numeric
meta_table <- meta_table %>%
  mutate(across(.cols = c("il_2", "il_10", "il_13", "il_4"),
                .fns = as.numeric))

str(meta_table)
## tibble [60 × 89] (S3: tbl_df/tbl/data.frame)
##  $ patient_id                          : Factor w/ 12 levels "6101","6102",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ sex                                 : Factor w/ 2 levels "F","M": 1 2 1 2 2 2 2 1 2 2 ...
##  $ age_at_enrollment                   : num [1:60] 58 65 60 54 40 57 68 75 36 46 ...
##  $ bmi_at_enrollment                   : num [1:60] 31.1 36.8 30.3 33.1 30.4 ...
##  $ intervention                        : Factor w/ 3 levels "Baseline","Red",..: 2 2 2 3 2 3 2 3 3 3 ...
##  $ sequence                            : Factor w/ 2 levels "R_Y","Y_R": 1 1 1 2 1 2 1 2 2 2 ...
##  $ intervention_week                   : Factor w/ 5 levels "0","2","6","10",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ pre_post                            : Factor w/ 3 levels "baseline","post",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ period                              : Factor w/ 5 levels "B0","B1","B2",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ if_ng                               : num [1:60] 1.16 11.74 0.93 0.63 1.88 ...
##  $ il_1b                               : num [1:60] 15.26 28.75 8.62 11.04 22.5 ...
##  $ il_2                                : num [1:60] 0.98 9.32 0.55 0.65 1.8 0.35 0.38 0.05 NA 0.16 ...
##  $ il_6                                : num [1:60] 2.18 5.83 2.53 1.29 2.25 ...
##  $ il_8                                : num [1:60] 2.86 4.35 3.93 1.93 4.14 3.05 1.35 2.01 4.43 2.86 ...
##  $ il_10                               : num [1:60] 0.05 0.71 1.89 7.17 0.75 2.2 1.91 4.26 0.02 0.48 ...
##  $ il_12p70                            : num [1:60] 2.86 60.93 5.46 2.79 4.06 ...
##  $ mcp_1                               : num [1:60] 51.9 189.3 224.9 244.7 167.9 ...
##  $ tn_fa                               : num [1:60] 14.9 92.3 26.5 19.6 49.6 ...
##  $ il_13                               : num [1:60] 9.64 92.04 21.61 23.17 60.22 ...
##  $ il_5                                : num [1:60] 3 13.04 2.74 5.45 5.38 ...
##  $ il_1ra                              : num [1:60] 3.52 11.36 3.71 3.38 6.08 ...
##  $ il_12p40                            : num [1:60] 51.5 139.4 81 78.7 175.9 ...
##  $ gm_csf                              : num [1:60] 10.9 195.7 17.9 27.4 72.4 ...
##  $ il_4                                : num [1:60] NA 8.8 1.6 4.49 1.22 1.12 0.31 0.91 0.07 NA ...
##  $ x01_cd45_cd66b_lymph_dc_mono        : num [1:60] 99.3 95.6 99.7 99.7 96.4 ...
##  $ x02_cd45_cd66b_grans                : num [1:60] 0.682 0.33 0.305 0.285 0.696 ...
##  $ cd45_cd14                           : num [1:60] 46.9 71 69.8 55.8 42.4 ...
##  $ cd45_cd20                           : num [1:60] 44.2 59.9 60.6 54 31.9 ...
##  $ x03_cd3_cd45_cd3_t_cells            : num [1:60] 25.7 43.7 54.5 14.2 14.3 ...
##  $ x04_tc_rgd_cd3_ab_t_cells           : num [1:60] 22 42.6 53.1 11.2 13.6 ...
##  $ x05_cd4_cd8_cd8_t_cells             : num [1:60] 11.96 3.84 13.23 6.5 5.72 ...
##  $ ccr7_cd8                            : num [1:60] 4.385 1.54 4.186 0.962 1.882 ...
##  $ ccr7_cd8_2                          : num [1:60] 7.56 2.28 9.04 5.52 3.83 ...
##  $ x06_cd45ro_cd45ra_naive_cd8         : num [1:60] 3.692 0.233 3.753 0.652 1.667 ...
##  $ x07_cd46ro_cd45ra_cm_cd8            : num [1:60] 0.506 0.955 0.263 0.196 0.146 ...
##  $ x08_cd45ro_cd45ra_em_cd8            : num [1:60] 6.555 0.395 7.505 4.528 1.807 ...
##  $ x09_cd45r0_cd45ra_te_cd8            : num [1:60] 0.826 1.322 1.2 0.775 1.661 ...
##  $ x10_cd38_hladr_activated_cd8        : num [1:60] 0.2543 0.0823 0.1523 0.1347 0.0742 ...
##  $ x11_cd4_cd8_cd4_t_cells             : num [1:60] 9.14 37.12 37.51 3.64 6.98 ...
##  $ ccr7_cd4                            : num [1:60] 7.98 33.92 32.87 2.49 5.3 ...
##  $ ccr7_cd4_2                          : num [1:60] 1.16 3.19 4.64 1.15 1.68 ...
##  $ x12_cd45ro_cd45ra_naive_cd4         : num [1:60] 3.163 3.77 17.23 0.693 1.731 ...
##  $ x13_cd45ro_cd45ra_cm_cd4            : num [1:60] 3.51 22.39 8.5 1.25 2.63 ...
##  $ x14_cd45ro_cd45ra                   : num [1:60] 1.053 2.964 3.919 0.893 1.488 ...
##  $ x15_cd45ro_cd45ra_te_cd4            : num [1:60] 0.0257 0.0664 0.5457 0.1154 0.092 ...
##  $ x16_cd38_hladr_activated_cd4        : num [1:60] 0.321 0.698 0.384 0.123 0.14 ...
##  $ cd45_cxcr5_th                       : num [1:60] 8.291 28.987 0.133 2.95 6.456 ...
##  $ x17_cd25_cd127_tregs                : num [1:60] 0.348 0.545 0.438 0.131 0.193 ...
##  $ x18_ccr4_cd4_total_ccr4_treg        : num [1:60] 0.317 0.494 0.316 0.123 0.18 ...
##  $ x19_cd45ra_cd45ro_ccr4_treg_naive   : num [1:60] 0.00973 0.03494 0.00676 0.00955 0.00744 ...
##  $ x21_cd45ra_cd45ro_ccr4_treg_memory  : num [1:60] 0.3017 0.4366 0.2905 0.0949 0.166 ...
##  $ x20_hladr_total_ccr4_treg_activated : num [1:60] 0.2867 0.2547 0.1017 0.0838 0.1119 ...
##  $ x22_cxcr3_ccr6_th1                  : num [1:60] 0.7981 2.0038 0.0209 0.4805 0.8362 ...
##  $ x23_cxcr3_ccr6_th2                  : num [1:60] 5.6202 13.4081 0.0747 1.2236 4.3881 ...
##  $ x24_cxcr3_ccr6_th17                 : num [1:60] 1.1118 9.5315 0.0153 0.6605 0.3389 ...
##  $ cd16_cd161                          : num [1:60] 27.1 46.9 55.1 15 23.5 ...
##  $ x25_cd19_cd3_b_cells                : num [1:60] 2.53 8.22 7.86 1.62 9.48 ...
##  $ cd20                                : num [1:60] 0.115 0.111 0.2 0.147 0.102 ...
##  $ cd20_2                              : num [1:60] 2.41 8.09 7.64 1.47 9.38 ...
##  $ x26_cd27_ig_d_naive_b_cells         : num [1:60] 1.19 6.99 4.62 1.29 6.69 ...
##  $ x27_cd27_ig_d_memory_b_cells        : num [1:60] 0.697 0.529 2.22 0.101 0.878 ...
##  $ x28_cd27_ig_d_memory_resting_b_cells: num [1:60] 0.1083 0.2849 0.304 0.0499 1.4241 ...
##  $ x30_cd27_cd38_plasmablasts          : num [1:60] 0.0634 0.0277 0.1582 0.0378 0.062 ...
##  $ cd19_cd3                            : num [1:60] 66.8 35.6 30.2 79.9 65.8 ...
##  $ x31_cd14_monocytes                  : num [1:60] 47.7 19.5 23.2 41.8 48.7 ...
##  $ x32_cd16_non_classical_mono         : num [1:60] 6.24 3.29 3.07 7.15 3.4 ...
##  $ x33_cd16_classical_mono             : num [1:60] 38.5 16.1 19.1 33.5 45.1 ...
##  $ x34_hladr_cd56                      : num [1:60] 43.8 16.7 20.3 33.9 41.1 ...
##  $ cd16_cd123                          : num [1:60] 1.142 0.272 0.657 0.645 0.613 ...
##  $ x35_cd16_cd123_cd11c_p_dc           : num [1:60] 0.709 0.248 0.594 0.37 0.495 ...
##  $ cd16_cd123_2                        : num [1:60] 33.1 10.5 15.1 25.6 37.3 ...
##  $ x36_cd16_cd123_cd11c_m_dc           : num [1:60] 27.7 10.4 14.7 23.6 37.1 ...
##  $ cd123                               : num [1:60] 65.7 35.1 29.5 73.5 65.1 ...
##  $ total_dc                            : num [1:60] 29.5 10.9 15.9 34 38.2 ...
##  $ x37_cd56_cd161_cd123_nk_cells       : num [1:60] 13.01 9.43 2.44 5.33 14.75 ...
##  $ x38_cd16_nk_cells                   : num [1:60] 1.601 1.552 0.874 28.656 5.754 ...
##  $ cd16_nk_cells                       : num [1:60] 11.41 7.85 1.56 41.75 8.9 ...
##  $ cd14_cd11b                          : num [1:60] 46.4 17.3 20.5 35 48.7 ...
##  $ x40_cd14_mdsc_mono                  : num [1:60] 28.983 7.399 9.73 0.215 39.928 ...
##  $ cd66b_cd11b                         : num [1:60] 0.314 0.251 0.265 0.195 0.532 ...
##  $ x41_cd66b_mdsc_grans                : num [1:60] 0.2057 0.0724 0.0985 NA 0.4165 ...
##  $ lutein                              : num [1:60] 735 120 556 419 240 ...
##  $ zeaxanthin                          : num [1:60] 94.6 140.1 99 81.6 53.7 ...
##  $ b_cryptoxanthin                     : num [1:60] 314 286 389 230 570 ...
##  $ a_carotene                          : num [1:60] 195.7 101.8 181.2 112.6 37.1 ...
##  $ b_carotene                          : num [1:60] 927 309 434 536 121 ...
##  $ other_cis_lyc                       : num [1:60] 151 276 161 165 273 ...
##  $ all_trans_lyc                       : num [1:60] 115 259 185 175 336 ...
##  $ x5_cis_lyc                          : num [1:60] 132 262 166 149 235 ...
# changing factor levels for pre_post
meta_table$pre_post <- factor(meta_table$pre_post,
                              levels = c("pre", "post"))

levels(meta_table$pre_post)  
## [1] "pre"  "post"
# Calculate total_cis_lyc, total_lyc, and total_carotenoids
meta_table <- meta_table %>%
  rename(n5_cis_lyc = x5_cis_lyc) %>%
  mutate(total_cis_lyc = other_cis_lyc + n5_cis_lyc,
         total_lyc = all_trans_lyc + total_cis_lyc,
         total_carotenoids = lutein + zeaxanthin + b_cryptoxanthin + 
                             a_carotene + b_carotene + total_lyc) 

Carotenoids

All-trans-lyc levels

# line plots for each subject at each timepoint
meta_table %>% 
  ggplot(aes(x = intervention_week, y = all_trans_lyc, color = intervention)) +
  geom_point() + 
  geom_line(aes(group = intervention)) +
  scale_color_manual(values = c("Baseline" = "gray", 
                                           "Yellow" = "gold",
                                           "Red" = "tomato1")) +
  facet_wrap(vars(patient_id), scales = "free_y") + 
  theme_bw() +
  labs(x = "Intervention Week",
       y = "All-trans-lycopene levels (nmol/L)",
       title = "All-trans-lycopene levels in each patient before/after each intervention")

Total cis-lyc levels

# line plots for each subject at each timepoint
meta_table %>% 
  ggplot(aes(x = intervention_week, y = total_cis_lyc, color = intervention)) +
  geom_point() + 
  geom_line(aes(group = intervention)) +
  scale_color_manual(values = c("Baseline" = "gray", 
                                           "Yellow" = "gold",
                                           "Red" = "tomato1")) +
  facet_wrap(vars(patient_id)) +
  theme_bw() +
  labs(x = "Intervention Week",
       y = "Total cis-lycopene levels (nmol/L)",
       title = "Total cis-lycopene levels in each patient before/after each intervention")

Total lyc levels

lineplots

# line plots for each subject at each timepoint
meta_table %>% 
  ggplot(aes(x = intervention_week, y = total_lyc, color = intervention)) +
  geom_point() + 
  geom_line(aes(group = intervention)) +
  scale_color_manual(values = c("Baseline" = "gray", 
                                           "Yellow" = "gold",
                                           "Red" = "tomato1")) +
  facet_wrap(vars(patient_id), scales = "free_y") +
  theme_bw() +
  labs(x = "Intervention Week",
       y = "Total lycopene levels (nmol/L)",
       title = "Total lycopene levels in each patient before/after each intervention")

Boxplots

wrangling

# create a more specific pre_post_intervention column
meta_table_edited <- meta_table %>%
  unite(col = "pre_post_intervention",
        c("pre_post","intervention"),
        sep = "_",
        remove = FALSE)

# make pre_post_intervention column factors
meta_table_edited$pre_post_intervention <- as.factor(meta_table_edited$pre_post_intervention)

# relevel factor columns
meta_table_edited$pre_post_intervention <- factor(meta_table_edited$pre_post_intervention, levels = c("pre_Yellow", "post_Yellow", "pre_Red", "post_Red"))

meta_table_edited$intervention <- factor(meta_table_edited$intervention,
                                         levels = c("Yellow", "Red"))
# make legend title
legendtitle_ppintervention <- "Timepoint"


# labels
labs_ppintervention <- c("pre control",
                         "post control",
                         "pre Tomato-Soy",
                         "post Tomato-Soy")

figure

meta_table_edited %>% 
  filter(intervention != "Baseline") %>%
  ggplot(aes(x = intervention, y = total_lyc, fill = pre_post_intervention)) +
  geom_boxplot(outlier.shape = NA) + 
  scale_fill_manual(legendtitle_ppintervention,
                    values = c("pre_Red" = "#FF9966",
                               "post_Red" = "#FF3300",
                               "pre_Yellow" = "#FFFF99",
                               "post_Yellow" = "yellow"),
                    labels = labs_ppintervention) +
  theme_clean() +
  labs(x = "",
       y = "Total lycopene levels (nmol/L)",
       title = "Total lycopene levels before/after juice interventions")

meta_table_edited %>% 
  filter(intervention != "Baseline") %>%
  ggplot(aes(x = pre_post_intervention, y = total_lyc, fill = intervention)) +
  geom_boxplot(outlier.shape = NA, show.legend = FALSE) + 
  scale_fill_manual(values = c("Red" = "tomato1",
                               "Yellow" = "yellow1")) +
  scale_x_discrete(labels = labs_ppintervention) +
  theme_clean(base_size = 22, base_family = "sans") +
  labs(x = "",
       y = "Overall plasma lycopene conc. (nmol/L)",
       title = "Total plasma lycopene levels",
       subtitle = "Before and after juice interventions")

# publish-ready plot
library(ggpubr)

(total_lyc_levels <- meta_table_edited %>% 
    filter(intervention != "Baseline") %>%
  ggpaired(x = "pre_post", y = "total_lyc", fill = "intervention", line.color = "gray", line.size = 1, facet.by = "intervention", short.panel.labs = FALSE, panel.labs = list(intervention = c("", ""))) +
  scale_fill_manual(values = c("Red" = "tomato1",
                               "Yellow" = "yellow1"),
                    labels = c("Control", "Tomato-Soy"),
                    name = "Intervention") +
  geom_line(aes(group = patient_id), colour = "gray", linewidth = 0.15) +
  theme_clean(base_size = 18, base_family = "sans") +
  labs(x = "",
       y = "nmol/L plasma",
       title = "Concentration of Lycopene",
       subtitle = ""))

export

ggsave(filename = "/Users/mariasholola/Documents/GitHub/USDA-Inflammation-Metabolomics/Plots/total-lyc-red-and-yellow-boxplots.svg", plot = total_lyc_levels, width = 12)

Descriptive stats

wrangle

# convert meta_table_edited to long format for lycopene
meta_table_lyc_long <- meta_table_edited %>%
  pivot_longer(cols = total_lyc,
               names_to = "total_lycopene",
               values_to = "nmol_per_L")

Avg/std dev

tomsoy
meta_table_lyc_long %>%
  filter(intervention == "Red") %>%
  group_by(pre_post) %>%
  summarize(mean = mean(nmol_per_L),
            stdev = sd(nmol_per_L))
## # A tibble: 2 × 3
##   pre_post  mean stdev
##   <fct>    <dbl> <dbl>
## 1 pre       523.  228.
## 2 post     1298.  665.
yellow
meta_table_lyc_long %>%
  filter(intervention == "Yellow") %>%
  group_by(pre_post) %>%
  summarize(mean = mean(nmol_per_L),
            stdev = sd(nmol_per_L))
## # A tibble: 2 × 3
##   pre_post  mean stdev
##   <fct>    <dbl> <dbl>
## 1 pre       721.  377.
## 2 post      703.  434.

Mean fold changes

lyc_long_subset <- meta_table_lyc_long %>%
  select(patient_id, nmol_per_L, pre_post_intervention) %>%
  pivot_wider(names_from = pre_post_intervention,
              values_from = nmol_per_L) %>%
  mutate(red_FC = post_Red/pre_Red,
         yellow_FC = post_Yellow/pre_Yellow)

lyc_long_subset %>%
  summarize(mean_red_FC = mean(red_FC),
            mean_yellow_FC = mean(yellow_FC))
## # A tibble: 1 × 2
##   mean_red_FC mean_yellow_FC
##         <dbl>          <dbl>
## 1        2.83          0.966

Normality checks

Plot histogram

gghistogram(meta_table_lyc_long$nmol_per_L, bins = 40)

Shapiro’s normality test

# shapiro normality test for total lycopene 

meta_table_lyc_long %>%
  group_by(intervention) %>%
  shapiro_test(vars = "nmol_per_L")
## # A tibble: 3 × 4
##   intervention variable   statistic       p
##   <fct>        <chr>          <dbl>   <dbl>
## 1 Yellow       nmol_per_L     0.876 0.00698
## 2 Red          nmol_per_L     0.848 0.00202
## 3 <NA>         nmol_per_L     0.913 0.233

P val for shapiro test turned out to < 0.05 for both interventions, suggesting data here is not normal. Let’s log transform before running paired t-tests.

Log transform

meta_table_lyc_log2_long <- meta_table_lyc_long %>%
  mutate(log2_nmol_per_L = log2(nmol_per_L))

Without transforming, here’s a paired t-test

compare_means(nmol_per_L ~ pre_post, meta_table_lyc_log2_long, method = "t.test", paired = TRUE, group.by = "intervention")
## # A tibble: 2 × 9
##   intervention .y.        group1 group2       p  p.adj p.format p.signif method
##   <fct>        <chr>      <chr>  <chr>    <dbl>  <dbl> <chr>    <chr>    <chr> 
## 1 Red          nmol_per_L pre    post   0.00104 0.0021 0.001    **       T-test
## 2 Yellow       nmol_per_L pre    post   0.709   0.71   0.709    ns       T-test
normality check

Plot histogram

gghistogram(meta_table_lyc_log2_long$log2_nmol_per_L, bins = 40)

Shapiro’s normality test

# shapiro normality test for total lycopene 
meta_table_lyc_log2_long %>%
  group_by(intervention) %>%
  shapiro_test(vars = "log2_nmol_per_L")
## # A tibble: 3 × 4
##   intervention variable        statistic      p
##   <fct>        <chr>               <dbl>  <dbl>
## 1 Yellow       log2_nmol_per_L     0.928 0.0869
## 2 Red          log2_nmol_per_L     0.964 0.521 
## 3 <NA>         log2_nmol_per_L     0.903 0.173
paired t test
compare_means(log2_nmol_per_L ~ pre_post, meta_table_lyc_log2_long, method = "t.test", paired = TRUE, group.by = "intervention")
## # A tibble: 2 × 9
##   intervention .y.        group1 group2       p   p.adj p.format p.signif method
##   <fct>        <chr>      <chr>  <chr>    <dbl>   <dbl> <chr>    <chr>    <chr> 
## 1 Red          log2_nmol… pre    post   3.84e-5 7.70e-5 3.8e-05  ****     T-test
## 2 Yellow       log2_nmol… pre    post   3.21e-1 3.2 e-1 0.32     ns       T-test

Total lycopene levels are significantly increasing only after post-Red intervention

Cytokines

# convert cytokines from wide to long
cytokines_long <- meta_table[-c(25:81)] %>%
  pivot_longer(cols = if_ng:il_4,
               names_to = "cytokines",
               values_to = "cyto_conc_pg_ml")
#cytokines_long_nona <- na.omit(cytokines_long)

note 11/30/23: I took this part out since NAs don’t matter for lmm

Mixed linear modeling

After testing several models, the best model turned out to have pre_post as a fixed variable and patient_id as random effect. Carotenoids added no statistically significant effect to the model, suggesting that carotenoids are not contributing to cytokine levels. Set REML = FALSE when comparing models.

Sequence effect test

# turn off sci notation
options(scipen = 999)

# sequence effect test lmer model function
cyto_seq_function <- function(cytokines_long) {
  lmer(cyto_conc_pg_ml ~ sequence + (1|patient_id), 
       data = cytokines_long, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cyto_seq_models <- map(split(cytokines_long,
                         cytokines_long$cytokines),
                   cyto_seq_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cyto_seq_results <- map_df(cyto_seq_models,
                          tidy,
                          .id = "cytokines")

# extract fixed coefficients for sequence only
cyto_seq_results_coefonly <- cyto_seq_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )
# results 
kable(cyto_seq_results_coefonly, format = "markdown", digits = 3)
cytokines effect group term estimate std.error statistic df p.value p.sig
gm_csf fixed NA sequenceY_R -27.444 24.681 -1.112 10.000 0.292 ns
if_ng fixed NA sequenceY_R -1.194 1.477 -0.808 10.000 0.438 ns
il_10 fixed NA sequenceY_R 1.728 1.220 1.416 9.996 0.187 ns
il_12p40 fixed NA sequenceY_R -16.115 23.825 -0.676 10.000 0.514 ns
il_12p70 fixed NA sequenceY_R -7.289 7.609 -0.958 10.000 0.361 ns
il_13 fixed NA sequenceY_R -18.773 13.629 -1.377 10.724 0.196 ns
il_1b fixed NA sequenceY_R -1.552 8.358 -0.186 10.000 0.856 ns
il_1ra fixed NA sequenceY_R -2.149 1.685 -1.275 10.000 0.231 ns
il_2 fixed NA sequenceY_R -1.585 1.440 -1.101 8.191 0.302 ns
il_4 fixed NA sequenceY_R -0.667 1.778 -0.375 6.000 0.721 ns
il_5 fixed NA sequenceY_R 1.657 3.027 0.547 10.000 0.596 ns
il_6 fixed NA sequenceY_R -2.432 1.896 -1.283 10.000 0.229 ns
il_8 fixed NA sequenceY_R 0.432 0.674 0.641 10.000 0.536 ns
mcp_1 fixed NA sequenceY_R 49.779 28.416 1.752 10.000 0.110 ns
tn_fa fixed NA sequenceY_R -6.730 10.112 -0.666 10.000 0.521 ns

Are there any significant cytokines?

# extract statistically significant cytokines 
cytokines_seq_psig <- cyto_seq_results_coefonly %>%
  filter(cyto_seq_results_coefonly$p.value < 0.05)

print(cytokines_seq_psig$cytokines)
## character(0)
  • No statistically significant cytokines, suggesting there are no sequence effects

Intervention comparison

# filter data for post-intervention only
cytokines_post_long <- cytokines_long %>%
  filter(intervention != "Baseline") %>%
  filter(pre_post == "post")
# treatment effect test lmer model function
cyto_trt_function <- function(cytokines_post_long) {
  lmer(cyto_conc_pg_ml ~ intervention + (1|patient_id), 
       data = cytokines_post_long, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cyto_trt_models <- map(split(cytokines_post_long,
                         cytokines_post_long$cytokines),
                   cyto_trt_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cyto_trt_results <- map_df(cyto_trt_models,
                          tidy,
                          .id = "cytokines")

# extract fixed coefficients for sequence only
cyto_trt_coefonly <- cyto_trt_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )

Results

kable(cyto_trt_coefonly, format = "markdown", digits = 3)
cytokines effect group term estimate std.error statistic df p.value p.sig
gm_csf fixed NA interventionYellow -2.237 2.904 -0.770 11.000 0.457 ns
if_ng fixed NA interventionYellow 0.081 0.213 0.380 11.000 0.711 ns
il_10 fixed NA interventionYellow 0.121 0.158 0.766 10.035 0.461 ns
il_12p40 fixed NA interventionYellow 6.302 5.595 1.126 11.000 0.284 ns
il_12p70 fixed NA interventionYellow -0.220 0.361 -0.609 11.000 0.555 ns
il_13 fixed NA interventionYellow -0.213 1.716 -0.124 7.124 0.905 ns
il_1b fixed NA interventionYellow 1.167 1.119 1.043 11.000 0.319 ns
il_1ra fixed NA interventionYellow 0.311 0.469 0.662 11.000 0.521 ns
il_2 fixed NA interventionYellow -0.153 0.168 -0.909 6.131 0.398 ns
il_4 fixed NA interventionYellow -0.129 0.075 -1.713 7.000 0.131 ns
il_5 fixed NA interventionYellow 0.500 0.708 0.706 11.000 0.495 ns
il_6 fixed NA interventionYellow 0.991 0.750 1.322 11.000 0.213 ns
il_8 fixed NA interventionYellow -0.060 0.355 -0.169 11.000 0.869 ns
mcp_1 fixed NA interventionYellow 3.200 8.404 0.381 11.000 0.711 ns
tn_fa fixed NA interventionYellow 0.458 1.097 0.418 11.000 0.684 ns
# extract statistically significant cytokines 
cytokines_trt_psig <- cyto_trt_coefonly %>%
  filter(cyto_trt_coefonly$p.value < 0.05)


print(cytokines_trt_psig$cytokines)
## character(0)
  • There are no cytokines with significantly different levels when comparing post-red vs. post-yellow treatment

Yellow treatment effects

# subset data into yellow results
cytokines_Y <- cytokines_long %>%
  filter(intervention == "Yellow")
# treatment effect test lmer model function
cyto_trtY_function <- function(cytokines_Y) {
  lmer(cyto_conc_pg_ml ~ pre_post + (1|patient_id), 
       data = cytokines_Y, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cyto_trtY_models <- map(split(cytokines_Y,
                         cytokines_Y$cytokines),
                   cyto_trtY_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cyto_trtY_results <- map_df(cyto_trtY_models,
                          tidy,
                          .id = "cytokines")

# extract fixed coefficients for sequence only
cyto_trtY_coefonly <- cyto_trtY_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )

Results

kable(cyto_trtY_coefonly, format = "markdown", digits = 3)
cytokines effect group term estimate std.error statistic df p.value p.sig
gm_csf fixed NA pre_postpost -10.773 9.940 -1.084 11.000 0.302 ns
if_ng fixed NA pre_postpost -0.582 0.554 -1.050 11.000 0.316 ns
il_10 fixed NA pre_postpost 0.003 0.144 0.024 9.060 0.982 ns
il_12p40 fixed NA pre_postpost -7.124 9.540 -0.747 11.000 0.471 ns
il_12p70 fixed NA pre_postpost -3.552 3.510 -1.012 11.000 0.333 ns
il_13 fixed NA pre_postpost -10.171 8.663 -1.174 7.959 0.274 ns
il_1b fixed NA pre_postpost -4.913 3.071 -1.600 11.000 0.138 ns
il_1ra fixed NA pre_postpost -1.743 1.465 -1.189 11.000 0.259 ns
il_2 fixed NA pre_postpost -1.097 0.952 -1.151 7.491 0.285 ns
il_4 fixed NA pre_postpost -0.752 0.722 -1.043 7.000 0.332 ns
il_5 fixed NA pre_postpost -0.906 1.413 -0.641 11.000 0.535 ns
il_6 fixed NA pre_postpost -0.614 0.659 -0.932 11.000 0.372 ns
il_8 fixed NA pre_postpost 0.130 0.296 0.439 11.000 0.669 ns
mcp_1 fixed NA pre_postpost 1.432 5.523 0.259 11.000 0.800 ns
tn_fa fixed NA pre_postpost -3.615 4.206 -0.859 11.000 0.408 ns

Significant cytokines

# extract statistically significant cytokines 
cytokines_trtY_psig <- cyto_trtY_coefonly %>%
  filter(cyto_trtY_coefonly$p.value < 0.05)


print(cytokines_trtY_psig$cytokines)
## character(0)
  • No significant cytokines within yellow treatment

Red treatment effects

# subset data into yellow results
cytokines_R <- cytokines_long %>%
  filter(intervention == "Red")
# treatment effect test lmer model function
cyto_trtR_function <- function(cytokines_R) {
  lmer(cyto_conc_pg_ml ~ pre_post + (1|patient_id), 
       data = cytokines_R, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cyto_trtR_models <- map(split(cytokines_R,
                         cytokines_R$cytokines),
                   cyto_trtR_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cyto_trtR_results <- map_df(cyto_trtR_models,
                          tidy,
                          .id = "cytokines")

# extract fixed coefficients for sequence only
cyto_trtR_coefonly <- cyto_trtR_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )

Results

kable(cyto_trtR_coefonly, format = "markdown", digits = 3) %>%
  row_spec(11, bold = TRUE)
cytokines effect group term estimate std.error statistic df p.value p.sig
gm_csf fixed NA pre_postpost -7.572 5.759 -1.315 11.000 0.215 ns
if_ng fixed NA pre_postpost -0.497 0.412 -1.209 11.000 0.252 ns
il_10 fixed NA pre_postpost -0.157 0.139 -1.131 10.028 0.284 ns
il_12p40 fixed NA pre_postpost -13.373 6.652 -2.010 11.000 0.070 ns
il_12p70 fixed NA pre_postpost -3.119 2.415 -1.291 11.000 0.223 ns
il_13 fixed NA pre_postpost -5.090 4.748 -1.072 7.669 0.316 ns
il_1b fixed NA pre_postpost -1.908 1.306 -1.461 11.000 0.172 ns
il_1ra fixed NA pre_postpost -0.435 0.453 -0.961 11.000 0.357 ns
il_2 fixed NA pre_postpost -0.522 0.556 -0.938 7.313 0.378 ns
il_4 fixed NA pre_postpost -0.581 0.527 -1.104 7.000 0.306 ns
il_5 fixed NA pre_postpost -1.808 0.815 -2.220 11.000 0.048 *
il_6 fixed NA pre_postpost -1.102 0.820 -1.343 11.000 0.206 ns
il_8 fixed NA pre_postpost 0.073 0.217 0.338 11.000 0.741 ns
mcp_1 fixed NA pre_postpost -2.799 6.896 -0.406 11.000 0.693 ns
tn_fa fixed NA pre_postpost -4.860 2.934 -1.656 11.000 0.126 ns

Significant cytokines

# extract statistically significant cytokines 
cytokines_trtR_psig <- cyto_trtR_coefonly %>%
  filter(cyto_trtR_coefonly$p.value < 0.05)

print(cytokines_trtR_psig$cytokines)
## [1] "il_5"
  • IL-5 is the only significantly different cytokine within red intervention. Same p value from t-test (= 0.0484)
  • IL-5 on average goes down by ~1.8 pg/mL from pre to post in red intervention

IL-5 plots

lineplots
meta_table %>%
  filter(intervention == "Red") %>%
  ggplot(aes(x = intervention_week, y = il_5, 
             color = intervention, group = intervention)) +
  geom_line() +
  theme_classic() +
  labs(x = "Week",
       y = "IL-5 levels (pg/mL)",
       title = "IL-5 changes by subject before and after red intervention",
       color = "Patient ID") +
  facet_wrap(~patient_id,
             scales = "free_y") +
  scale_color_manual(values = c("Red" = "tomato1"))

meta_table %>% 
  filter(intervention == "Red") %>%
  ggplot(aes(x = pre_post, y = il_5, color = patient_id)) +
  geom_line(aes(group = patient_id)) +
  labs(x = "",
       y = "IL-5 conc (pg/mL)",
       title = "IL-5 levels in each patient before and after red intervention") +
  theme_classic()

meta_table %>% 
  filter(intervention != "Baseline") %>%
  ggplot(aes(x = pre_post, y = il_5, color = intervention)) +
  geom_line(aes(group = intervention)) +
  scale_color_manual(values = c("Yellow" = "gold",
                                "Red" = "tomato1")) +
  facet_wrap(vars(patient_id), scales = "free_y") + 
  theme_classic() +
  labs(x = "",
       y = "IL-5 conc (pg/mL)",
       title = "IL-5 levels in each patient pre- and post- red and yellow intervention")

boxplots
cytokines_long %>%
  filter(intervention == "Red") %>%
  filter(cytokines == "il_5") %>%
  ggplot(aes(x = pre_post, y = cyto_conc_pg_ml, fill = intervention)) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter() +
  scale_fill_manual(values = c("Red" = "tomato1")) +
  labs(x = "",
       y = "IL- 5 levels (pg/mL)",
       title = "IL-5 levels before and after red intervention") +
  theme_bw()

  • IL-5 on average goes down by ~1.8 pg/mL from pre to post in red intervention. This is a significant decrease according to the mixed linear model.

Log-transformed cytokines

I know the data isn’t normally distributed. Mixed linear models do not assume normality of the dependant variable, however the residuals should be normally distributed.

resid normality checks

I need to figure out how to make a function that extracts makes a QQplot for each model. But since IL-5 was significant, let’s look at how the residuals look for this cytokine (for every comparison) before log transforming

#qqplot_fx <- function(cytokine){
  #qqnorm(resid(cyto_seq_models$cytokine))
  #qqline(resid(cyto_seq_models$cytokine))
#}

#qqplot_fx("il_5")

#cyto_seq_results$

seq

qqnorm(resid(cyto_seq_models$il_5))
qqline(resid(cyto_seq_models$il_5))

That doesn’t look good.

intervention

qqnorm(resid(cyto_trt_models$il_5))
qqline(resid(cyto_trt_models$il_5))

doesn’t look as bad as sequence

red

qqnorm(resid(cyto_trtR_models$il_5))
qqline(resid(cyto_trtR_models$il_5))

doesn’t look that bad for Red

yellow

qqnorm(resid(cyto_trtY_models$il_5))
qqline(resid(cyto_trtY_models$il_5))

I will log transform the data and perform mixed linear modeling and paired t-tests for every comparison to see how the data compares.

Wrangle

cytokines_log2conc_long <- cytokines_long %>%
  mutate(log2_cyto_conc_pg_ml = log2(cyto_conc_pg_ml))

Mixed linear modeling

Sequence effect test

We want to make sure there are no seq effects

# sequence effect test lmer model function
cyto_log2_seq_function <- function(cytokines_log2conc_long) {
  lmer(log2_cyto_conc_pg_ml ~ sequence + (1|patient_id), 
       data = cytokines_log2conc_long, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cyto_log2_seq_models <- map(split(cytokines_log2conc_long,
                         cytokines_log2conc_long$cytokines),
                   cyto_log2_seq_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cyto_log2_seq_results <- map_df(cyto_log2_seq_models,
                          tidy,
                          .id = "cytokines")

# extract fixed coefficients for sequence only
cyto_log2_seq_results_coefonly <- cyto_log2_seq_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )

Resid check

# before log transform
qqnorm(resid(cyto_seq_models$il_5))
qqline(resid(cyto_seq_models$il_5))

# after log transform
qqnorm(resid(cyto_log2_seq_models$il_5))
qqline(resid(cyto_log2_seq_models$il_5))

This looks better after log transform.

Results

# results 
kable(cyto_log2_seq_results_coefonly, format = "markdown", digits = 3)
cytokines effect group term estimate std.error statistic df p.value p.sig
gm_csf fixed NA sequenceY_R -0.703 0.813 -0.864 10.000 0.408 ns
if_ng fixed NA sequenceY_R -0.639 1.065 -0.600 10.000 0.562 ns
il_10 fixed NA sequenceY_R 0.897 1.380 0.650 9.855 0.530 ns
il_12p40 fixed NA sequenceY_R -0.181 0.408 -0.444 10.000 0.666 ns
il_12p70 fixed NA sequenceY_R -0.804 0.889 -0.905 10.000 0.387 ns
il_13 fixed NA sequenceY_R -1.081 0.848 -1.275 10.230 0.230 ns
il_1b fixed NA sequenceY_R -0.321 0.836 -0.384 10.000 0.709 ns
il_1ra fixed NA sequenceY_R -0.570 0.518 -1.101 10.000 0.297 ns
il_2 fixed NA sequenceY_R -1.546 1.196 -1.292 8.035 0.232 ns
il_4 fixed NA sequenceY_R -0.557 1.491 -0.374 6.000 0.721 ns
il_5 fixed NA sequenceY_R 0.115 0.595 0.194 10.000 0.850 ns
il_6 fixed NA sequenceY_R -0.976 0.607 -1.607 10.000 0.139 ns
il_8 fixed NA sequenceY_R 0.287 0.371 0.773 10.000 0.457 ns
mcp_1 fixed NA sequenceY_R 0.469 0.304 1.543 10.000 0.154 ns
tn_fa fixed NA sequenceY_R -0.033 0.404 -0.083 10.000 0.936 ns

Are there any significant cytokines?

# extract statistically significant cytokines 
cytokines_log2_seq_psig <- cyto_log2_seq_results_coefonly %>%
  filter(cyto_log2_seq_results_coefonly$p.value < 0.05)

print(cytokines_log2_seq_psig$cytokines)
## character(0)
  • No statistically significant cytokines, suggesting there are no sequence effects

Intervention comparison

# filter data for post-intervention only
cytokines_log2conc_post_long <- cytokines_log2conc_long %>%
  filter(intervention != "Baseline") %>%
  filter(pre_post == "post")
# treatment effect test lmer model function
cyto_log2_trt_function <- function(cytokines_log2conc_post_long) {
  lmer(log2_cyto_conc_pg_ml ~ intervention + (1|patient_id), 
       data = cytokines_log2conc_post_long, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cyto_log2_trt_models <- map(split(cytokines_log2conc_post_long,
                         cytokines_log2conc_post_long$cytokines),
                   cyto_log2_trt_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cyto_log2_trt_results <- map_df(cyto_log2_trt_models,
                          tidy,
                          .id = "cytokines")

# extract fixed coefficients for sequence only
cyto_log2_trt_coefonly <- cyto_log2_trt_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )
Resid check
# before log transform
qqnorm(resid(cyto_trt_models$il_5))
qqline(resid(cyto_trt_models$il_5))

# log transformed
qqnorm(resid(cyto_log2_trt_models$il_5))
qqline(resid(cyto_log2_trt_models$il_5))

Don’t know if this helps for intervention comparison

Results
kable(cyto_log2_trt_coefonly, format = "markdown", digits = 3)
cytokines effect group term estimate std.error statistic df p.value p.sig
gm_csf fixed NA interventionYellow 0.116 0.118 0.980 11.000 0.348 ns
if_ng fixed NA interventionYellow 0.161 0.230 0.700 11.000 0.498 ns
il_10 fixed NA interventionYellow 0.183 0.110 1.665 9.985 0.127 ns
il_12p40 fixed NA interventionYellow 0.177 0.153 1.159 11.000 0.271 ns
il_12p70 fixed NA interventionYellow 0.166 0.153 1.083 11.000 0.302 ns
il_13 fixed NA interventionYellow 0.146 0.146 0.997 7.041 0.352 ns
il_1b fixed NA interventionYellow 0.126 0.164 0.767 11.000 0.459 ns
il_1ra fixed NA interventionYellow 0.083 0.155 0.535 11.000 0.603 ns
il_2 fixed NA interventionYellow 0.216 0.291 0.745 6.079 0.484 ns
il_4 fixed NA interventionYellow -0.052 0.070 -0.743 7.000 0.482 ns
il_5 fixed NA interventionYellow 0.379 0.216 1.749 11.000 0.108 ns
il_6 fixed NA interventionYellow 0.331 0.263 1.260 11.000 0.234 ns
il_8 fixed NA interventionYellow -0.105 0.156 -0.674 11.000 0.514 ns
mcp_1 fixed NA interventionYellow 0.007 0.066 0.108 11.000 0.916 ns
tn_fa fixed NA interventionYellow 0.035 0.071 0.499 11.000 0.627 ns
# extract statistically significant cytokines 
cytokines_log2_trt_psig <- cyto_log2_trt_coefonly %>%
  filter(cyto_log2_trt_coefonly$p.value < 0.05)


print(cytokines_log2_trt_psig$cytokines)
## character(0)
  • There are no cytokines with significantly different levels when comparing post-red vs. post-yellow treatment

Yellow treatment effects

# subset data into yellow results
cytokines_Y_log2 <- cytokines_log2conc_long %>%
  filter(intervention == "Yellow")
# treatment effect test lmer model function
cyto_trtY_log2_function <- function(cytokines_Y_log2) {
  lmer(log2_cyto_conc_pg_ml ~ pre_post + (1|patient_id), 
       data = cytokines_Y_log2, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cyto_trtY_log2_models <- map(split(cytokines_Y_log2,
                         cytokines_Y_log2$cytokines),
                   cyto_trtY_log2_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cyto_trtY_log2_results <- map_df(cyto_trtY_log2_models,
                          tidy,
                          .id = "cytokines")

# extract fixed coefficients for sequence only
cyto_trtY_log2_coefonly <- cyto_trtY_log2_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )
Resid check
# before log transform 
qqnorm(resid(cyto_trtY_models$il_5))
qqline(resid(cyto_trtY_models$il_5))

# after log transform
qqnorm(resid(cyto_trtY_log2_models$il_5))
qqline(resid(cyto_trtY_log2_models$il_5))

Results
kable(cyto_trtY_log2_coefonly, format = "markdown", digits = 3)
cytokines effect group term estimate std.error statistic df p.value p.sig
gm_csf fixed NA pre_postpost -0.183 0.135 -1.353 11.000 0.203 ns
if_ng fixed NA pre_postpost -0.235 0.165 -1.423 11.000 0.182 ns
il_10 fixed NA pre_postpost -0.241 0.226 -1.066 8.920 0.314 ns
il_12p40 fixed NA pre_postpost -0.068 0.126 -0.543 11.000 0.598 ns
il_12p70 fixed NA pre_postpost -0.053 0.145 -0.365 11.000 0.722 ns
il_13 fixed NA pre_postpost -0.130 0.242 -0.539 6.933 0.607 ns
il_1b fixed NA pre_postpost -0.206 0.208 -0.991 11.000 0.343 ns
il_1ra fixed NA pre_postpost -0.213 0.220 -0.970 11.000 0.353 ns
il_2 fixed NA pre_postpost -0.029 0.338 -0.087 7.055 0.933 ns
il_4 fixed NA pre_postpost -0.121 0.192 -0.633 7.000 0.547 ns
il_5 fixed NA pre_postpost 0.124 0.239 0.518 11.000 0.615 ns
il_6 fixed NA pre_postpost -0.125 0.356 -0.350 11.000 0.733 ns
il_8 fixed NA pre_postpost -0.024 0.141 -0.170 11.000 0.868 ns
mcp_1 fixed NA pre_postpost -0.012 0.043 -0.278 11.000 0.786 ns
tn_fa fixed NA pre_postpost -0.082 0.094 -0.876 11.000 0.400 ns

Significant cytokines

# extract statistically significant cytokines 
cytokines_trtY_log2_psig <- cyto_trtY_log2_coefonly %>%
  filter(cyto_trtY_log2_coefonly$p.value < 0.05)


print(cytokines_trtY_log2_psig$cytokines)
## character(0)
  • No significant cytokines within yellow treatment

Red treatment effects

# subset data into yellow results
cytokines_R_log2 <- cytokines_log2conc_long %>%
  filter(intervention == "Red")
# treatment effect test lmer model function
cyto_trtR_log2_function <- function(cytokines_R_log2) {
  lmer(log2_cyto_conc_pg_ml ~ pre_post + (1|patient_id), 
       data = cytokines_R_log2, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cyto_trtR_log2_models <- map(split(cytokines_R_log2,
                         cytokines_R_log2$cytokines),
                   cyto_trtR_log2_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cyto_trtR_log2_results <- map_df(cyto_trtR_log2_models,
                          tidy,
                          .id = "cytokines")

# extract fixed coefficients for sequence only
cyto_trtR_log2coefonly <- cyto_trtR_log2_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )
Resid check
# before log transform
qqnorm(resid(cyto_trtR_models$il_5))
qqline(resid(cyto_trtR_models$il_5))

# log transformed
qqnorm(resid(cyto_trtR_log2_models$il_5))
qqline(resid(cyto_trtR_log2_models$il_5))

Results
kable(cyto_trtR_log2coefonly, format = "markdown", digits = 3) 
cytokines effect group term estimate std.error statistic df p.value p.sig
gm_csf fixed NA pre_postpost -0.308 0.103 -2.985 11.000 0.012 *
if_ng fixed NA pre_postpost -0.307 0.197 -1.559 11.000 0.147 ns
il_10 fixed NA pre_postpost -0.137 0.422 -0.325 9.926 0.752 ns
il_12p40 fixed NA pre_postpost -0.288 0.156 -1.840 11.000 0.093 ns
il_12p70 fixed NA pre_postpost -0.622 0.240 -2.596 11.000 0.025 *
il_13 fixed NA pre_postpost -0.130 0.199 -0.652 7.181 0.535 ns
il_1b fixed NA pre_postpost -0.227 0.164 -1.387 11.000 0.193 ns
il_1ra fixed NA pre_postpost -0.109 0.120 -0.912 11.000 0.381 ns
il_2 fixed NA pre_postpost -0.273 0.277 -0.988 7.142 0.356 ns
il_4 fixed NA pre_postpost -0.200 0.156 -1.282 7.000 0.241 ns
il_5 fixed NA pre_postpost -0.373 0.172 -2.166 11.000 0.053 ns
il_6 fixed NA pre_postpost -0.200 0.161 -1.240 11.000 0.241 ns
il_8 fixed NA pre_postpost 0.068 0.080 0.842 11.000 0.418 ns
mcp_1 fixed NA pre_postpost 0.006 0.047 0.126 11.000 0.902 ns
tn_fa fixed NA pre_postpost -0.174 0.084 -2.070 11.000 0.063 ns

Significant cytokines

# extract statistically significant cytokines 
cytokines_trtR_log2_psig <- cyto_trtR_log2coefonly %>%
  filter(cyto_trtR_log2coefonly$p.value < 0.05)

print(cytokines_trtR_log2_psig$cytokines)
## [1] "gm_csf"   "il_12p70"

note 11/30/23: Log transforming changes results a bit. IL-5 is no longer significant (pval = 0.053), and now GM-CSF and IL-12p70 are significant. I’ve done nonparametric stats on untransformed data, these 2 cytokines along with IL-5 were significant. The question is, should I be doing log transformation for the mixed linear models? Q-Q plot looks better for IL-5 after log transforming…

Paired t tests

Red

compare_means(log2_cyto_conc_pg_ml ~ pre_post, cytokines_R_log2, method = "t.test", paired = TRUE, group.by = "cytokines")
## # A tibble: 15 × 9
##    cytokines .y.             group1 group2      p p.adj p.format p.signif method
##    <chr>     <chr>           <chr>  <chr>   <dbl> <dbl> <chr>    <chr>    <chr> 
##  1 if_ng     log2_cyto_conc… pre    post   0.147   1    0.147    ns       T-test
##  2 il_1b     log2_cyto_conc… pre    post   0.193   1    0.193    ns       T-test
##  3 il_2      log2_cyto_conc… pre    post   0.350   1    0.350    ns       T-test
##  4 il_6      log2_cyto_conc… pre    post   0.241   1    0.241    ns       T-test
##  5 il_8      log2_cyto_conc… pre    post   0.418   1    0.418    ns       T-test
##  6 il_10     log2_cyto_conc… pre    post   0.847   1    0.847    ns       T-test
##  7 il_12p70  log2_cyto_conc… pre    post   0.0249  0.35 0.025    *        T-test
##  8 mcp_1     log2_cyto_conc… pre    post   0.902   1    0.902    ns       T-test
##  9 tn_fa     log2_cyto_conc… pre    post   0.0628  0.75 0.063    ns       T-test
## 10 il_13     log2_cyto_conc… pre    post   0.540   1    0.540    ns       T-test
## 11 il_5      log2_cyto_conc… pre    post   0.0531  0.69 0.053    ns       T-test
## 12 il_1ra    log2_cyto_conc… pre    post   0.381   1    0.381    ns       T-test
## 13 il_12p40  log2_cyto_conc… pre    post   0.0928  1    0.093    ns       T-test
## 14 gm_csf    log2_cyto_conc… pre    post   0.0124  0.19 0.012    *        T-test
## 15 il_4      log2_cyto_conc… pre    post   0.241   1    0.241    ns       T-test

We get the same result for paired t-test on log transformed data

Immune Cells

Mixed linear modeling

note 11/30/23: if there are any NAs, should I assume below LOD? Or were values not recorded?

# convert immune cell data from wide to long
cells_long <- meta_table %>%
  select(1:24, starts_with("x")) %>%
  pivot_longer(cols = x01_cd45_cd66b_lymph_dc_mono:x41_cd66b_mdsc_grans,
               names_to = "cell_type",
               values_to = "cell_value")

Sequence effect test

# lmer model function
cells_seq_function <- function(cells_long) {
  lmer(cell_value ~ sequence + (1|patient_id), data = cells_long, REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cells_seq_models <- map(split(cells_long,
                              cells_long$cell_type),
                   cells_seq_function)

# create single data frame of results. apply tidy from broom.mixed package to clean up results
cells_seq_results <- map_df(cells_seq_models,
                            tidy,
                            .id = "cell_type")

cells_seq_results_coef <- cells_seq_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )

Results

kable(cells_seq_results_coef, format = "markdown", digits = 3)
cell_type effect group term estimate std.error statistic df p.value p.sig
x01_cd45_cd66b_lymph_dc_mono fixed NA sequenceY_R -0.024 2.152 -0.011 10.000 0.991 ns
x02_cd45_cd66b_grans fixed NA sequenceY_R 0.445 0.558 0.797 10.000 0.444 ns
x03_cd3_cd45_cd3_t_cells fixed NA sequenceY_R 5.038 7.441 0.677 10.000 0.514 ns
x04_tc_rgd_cd3_ab_t_cells fixed NA sequenceY_R 4.702 7.682 0.612 10.000 0.554 ns
x05_cd4_cd8_cd8_t_cells fixed NA sequenceY_R 3.751 2.477 1.514 10.000 0.161 ns
x06_cd45ro_cd45ra_naive_cd8 fixed NA sequenceY_R 1.338 1.832 0.730 10.000 0.482 ns
x07_cd46ro_cd45ra_cm_cd8 fixed NA sequenceY_R 0.152 0.210 0.724 10.000 0.485 ns
x08_cd45ro_cd45ra_em_cd8 fixed NA sequenceY_R 1.094 1.512 0.723 10.000 0.486 ns
x09_cd45r0_cd45ra_te_cd8 fixed NA sequenceY_R -0.109 0.437 -0.249 10.000 0.809 ns
x10_cd38_hladr_activated_cd8 fixed NA sequenceY_R -0.002 0.026 -0.089 10.000 0.931 ns
x11_cd4_cd8_cd4_t_cells fixed NA sequenceY_R 0.546 7.572 0.072 10.000 0.944 ns
x12_cd45ro_cd45ra_naive_cd4 fixed NA sequenceY_R 1.412 4.877 0.290 10.000 0.778 ns
x13_cd45ro_cd45ra_cm_cd4 fixed NA sequenceY_R -0.649 3.423 -0.190 10.000 0.853 ns
x14_cd45ro_cd45ra fixed NA sequenceY_R 0.835 0.737 1.133 10.000 0.284 ns
x15_cd45ro_cd45ra_te_cd4 fixed NA sequenceY_R -0.337 0.565 -0.597 10.001 0.564 ns
x16_cd38_hladr_activated_cd4 fixed NA sequenceY_R 0.001 0.087 0.011 10.000 0.991 ns
x17_cd25_cd127_tregs fixed NA sequenceY_R 0.290 0.296 0.978 10.000 0.351 ns
x18_ccr4_cd4_total_ccr4_treg fixed NA sequenceY_R 0.312 0.268 1.163 10.000 0.272 ns
x19_cd45ra_cd45ro_ccr4_treg_naive fixed NA sequenceY_R 0.019 0.011 1.802 10.000 0.102 ns
x20_hladr_total_ccr4_treg_activated fixed NA sequenceY_R 0.008 0.061 0.137 10.000 0.893 ns
x21_cd45ra_cd45ro_ccr4_treg_memory fixed NA sequenceY_R 0.224 0.204 1.097 10.000 0.298 ns
x22_cxcr3_ccr6_th1 fixed NA sequenceY_R 0.634 0.708 0.896 10.000 0.391 ns
x23_cxcr3_ccr6_th2 fixed NA sequenceY_R 7.379 5.644 1.307 10.000 0.220 ns
x24_cxcr3_ccr6_th17 fixed NA sequenceY_R 0.684 2.080 0.329 10.000 0.749 ns
x25_cd19_cd3_b_cells fixed NA sequenceY_R 1.080 2.731 0.395 10.000 0.701 ns
x26_cd27_ig_d_naive_b_cells fixed NA sequenceY_R 1.249 2.409 0.518 10.000 0.615 ns
x27_cd27_ig_d_memory_b_cells fixed NA sequenceY_R -0.236 0.322 -0.733 10.000 0.480 ns
x28_cd27_ig_d_memory_resting_b_cells fixed NA sequenceY_R -0.177 0.219 -0.806 10.000 0.439 ns
x30_cd27_cd38_plasmablasts fixed NA sequenceY_R -0.033 0.021 -1.613 10.000 0.138 ns
x31_cd14_monocytes fixed NA sequenceY_R -5.481 6.350 -0.863 10.000 0.408 ns
x32_cd16_non_classical_mono fixed NA sequenceY_R -0.440 0.703 -0.627 10.000 0.545 ns
x33_cd16_classical_mono fixed NA sequenceY_R -4.477 5.667 -0.790 10.000 0.448 ns
x34_hladr_cd56 fixed NA sequenceY_R -4.413 5.412 -0.815 10.000 0.434 ns
x35_cd16_cd123_cd11c_p_dc fixed NA sequenceY_R -0.092 0.069 -1.346 10.000 0.208 ns
x36_cd16_cd123_cd11c_m_dc fixed NA sequenceY_R -3.230 4.565 -0.708 10.000 0.495 ns
x37_cd56_cd161_cd123_nk_cells fixed NA sequenceY_R -1.323 2.273 -0.582 10.000 0.573 ns
x38_cd16_nk_cells fixed NA sequenceY_R 1.153 2.027 0.569 10.000 0.582 ns
x40_cd14_mdsc_mono fixed NA sequenceY_R -4.353 3.288 -1.324 10.000 0.215 ns
x41_cd66b_mdsc_grans fixed NA sequenceY_R -0.035 0.128 -0.274 10.000 0.789 ns
# extract statistically significant cytokines 
cells_seq_psig <- cells_seq_results_coef %>%
  filter(cells_seq_results_coef$p.value < 0.05)

print(cells_seq_psig$cell_type)
## character(0)

*No sequence effects

Intervention comparison

# filter data for post-intervention only
cells_post_long <- cells_long %>%
  filter(intervention != "Baseline") %>%
  filter(pre_post == "post")

str(cells_post_long)
## tibble [936 × 26] (S3: tbl_df/tbl/data.frame)
##  $ patient_id       : Factor w/ 12 levels "6101","6102",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ sex              : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
##  $ age_at_enrollment: num [1:936] 58 58 58 58 58 58 58 58 58 58 ...
##  $ bmi_at_enrollment: num [1:936] 31.1 31.1 31.1 31.1 31.1 ...
##  $ intervention     : Factor w/ 3 levels "Baseline","Red",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ sequence         : Factor w/ 2 levels "R_Y","Y_R": 1 1 1 1 1 1 1 1 1 1 ...
##  $ intervention_week: Factor w/ 5 levels "0","2","6","10",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ pre_post         : Factor w/ 2 levels "pre","post": 2 2 2 2 2 2 2 2 2 2 ...
##  $ period           : Factor w/ 5 levels "B0","B1","B2",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ if_ng            : num [1:936] 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 ...
##  $ il_1b            : num [1:936] 17.9 17.9 17.9 17.9 17.9 ...
##  $ il_2             : num [1:936] 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 ...
##  $ il_6             : num [1:936] 2.79 2.79 2.79 2.79 2.79 2.79 2.79 2.79 2.79 2.79 ...
##  $ il_8             : num [1:936] 3.53 3.53 3.53 3.53 3.53 3.53 3.53 3.53 3.53 3.53 ...
##  $ il_10            : num [1:936] 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 ...
##  $ il_12p70         : num [1:936] 2.79 2.79 2.79 2.79 2.79 2.79 2.79 2.79 2.79 2.79 ...
##  $ mcp_1            : num [1:936] 60.4 60.4 60.4 60.4 60.4 ...
##  $ tn_fa            : num [1:936] 16.8 16.8 16.8 16.8 16.8 ...
##  $ il_13            : num [1:936] 7.47 7.47 7.47 7.47 7.47 7.47 7.47 7.47 7.47 7.47 ...
##  $ il_5             : num [1:936] 3.74 3.74 3.74 3.74 3.74 3.74 3.74 3.74 3.74 3.74 ...
##  $ il_1ra           : num [1:936] 4.29 4.29 4.29 4.29 4.29 4.29 4.29 4.29 4.29 4.29 ...
##  $ il_12p40         : num [1:936] 45.3 45.3 45.3 45.3 45.3 ...
##  $ gm_csf           : num [1:936] 6.46 6.46 6.46 6.46 6.46 6.46 6.46 6.46 6.46 6.46 ...
##  $ il_4             : num [1:936] NA NA NA NA NA NA NA NA NA NA ...
##  $ cell_type        : chr [1:936] "x01_cd45_cd66b_lymph_dc_mono" "x02_cd45_cd66b_grans" "x03_cd3_cd45_cd3_t_cells" "x04_tc_rgd_cd3_ab_t_cells" ...
##  $ cell_value       : num [1:936] 98.61 1.36 36.48 31.92 15.05 ...
# remove x41_cd66b_mdsc_grans because it's causing an error
cells_post_long_edited <- cells_post_long %>%
  filter(cell_type != "x41_cd66b_mdsc_grans")
# treatment effect test lmer model function
cells_trt_function <- function(cells_post_long_edited) {
  lmer(cell_value ~ intervention + (1|patient_id), 
       data = cells_post_long_edited, 
       REML = TRUE)
}

# break data up into subsets based on cell, then apply funtion to each subset
cells_trt_models <- map(split(cells_post_long_edited,
                              cells_post_long_edited$cell_type),
                        cells_trt_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cells_trt_results <- map_df(cells_trt_models,
                           tidy,
                          .id = "cell_type")

# extract fixed coefficients for sequence only
cells_trt_coefonly <- cells_trt_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )

kable(cells_trt_coefonly, format = "markdown", digits = 4)
cell_type effect group term estimate std.error statistic df p.value p.sig
x01_cd45_cd66b_lymph_dc_mono fixed NA interventionYellow 0.1437 1.5446 0.0930 11 0.9276 ns
x02_cd45_cd66b_grans fixed NA interventionYellow -0.3867 0.3053 -1.2669 11 0.2313 ns
x03_cd3_cd45_cd3_t_cells fixed NA interventionYellow -1.9095 4.0782 -0.4682 11 0.6488 ns
x04_tc_rgd_cd3_ab_t_cells fixed NA interventionYellow -2.0179 4.0621 -0.4968 11 0.6291 ns
x05_cd4_cd8_cd8_t_cells fixed NA interventionYellow -0.0913 1.2534 -0.0729 11 0.9432 ns
x06_cd45ro_cd45ra_naive_cd8 fixed NA interventionYellow 0.1903 0.6862 0.2773 11 0.7867 ns
x07_cd46ro_cd45ra_cm_cd8 fixed NA interventionYellow -0.0137 0.0717 -0.1914 11 0.8517 ns
x08_cd45ro_cd45ra_em_cd8 fixed NA interventionYellow -0.6942 1.1263 -0.6164 11 0.5502 ns
x09_cd45r0_cd45ra_te_cd8 fixed NA interventionYellow 0.3789 0.2313 1.6379 11 0.1297 ns
x10_cd38_hladr_activated_cd8 fixed NA interventionYellow 0.0168 0.0119 1.4113 11 0.1858 ns
x11_cd4_cd8_cd4_t_cells fixed NA interventionYellow -1.6574 2.9437 -0.5630 11 0.5847 ns
x12_cd45ro_cd45ra_naive_cd4 fixed NA interventionYellow -0.6033 1.9806 -0.3046 11 0.7664 ns
x13_cd45ro_cd45ra_cm_cd4 fixed NA interventionYellow -0.1943 0.9081 -0.2140 11 0.8345 ns
x14_cd45ro_cd45ra fixed NA interventionYellow 0.0149 0.2516 0.0594 11 0.9537 ns
x15_cd45ro_cd45ra_te_cd4 fixed NA interventionYellow -1.0865 1.1069 -0.9816 22 0.3370 ns
x16_cd38_hladr_activated_cd4 fixed NA interventionYellow 0.0301 0.0376 0.8014 11 0.4398 ns
x17_cd25_cd127_tregs fixed NA interventionYellow 0.1232 0.0949 1.2986 11 0.2207 ns
x18_ccr4_cd4_total_ccr4_treg fixed NA interventionYellow 0.1147 0.0958 1.1970 11 0.2565 ns
x19_cd45ra_cd45ro_ccr4_treg_naive fixed NA interventionYellow 0.0058 0.0069 0.8350 11 0.4215 ns
x20_hladr_total_ccr4_treg_activated fixed NA interventionYellow 0.0527 0.0390 1.3536 11 0.2030 ns
x21_cd45ra_cd45ro_ccr4_treg_memory fixed NA interventionYellow 0.0754 0.0581 1.2975 11 0.2210 ns
x22_cxcr3_ccr6_th1 fixed NA interventionYellow -0.5504 0.6386 -0.8618 11 0.4072 ns
x23_cxcr3_ccr6_th2 fixed NA interventionYellow -0.4578 1.7276 -0.2650 11 0.7959 ns
x24_cxcr3_ccr6_th17 fixed NA interventionYellow 1.2526 1.1705 1.0701 11 0.3075 ns
x25_cd19_cd3_b_cells fixed NA interventionYellow 0.1449 0.8671 0.1671 11 0.8703 ns
x26_cd27_ig_d_naive_b_cells fixed NA interventionYellow 0.0560 0.8292 0.0676 11 0.9473 ns
x27_cd27_ig_d_memory_b_cells fixed NA interventionYellow 0.0936 0.2156 0.4343 11 0.6725 ns
x28_cd27_ig_d_memory_resting_b_cells fixed NA interventionYellow -0.0552 0.0831 -0.6645 11 0.5200 ns
x30_cd27_cd38_plasmablasts fixed NA interventionYellow 0.0185 0.0134 1.3743 11 0.1967 ns
x31_cd14_monocytes fixed NA interventionYellow 0.4091 3.8171 0.1072 11 0.9166 ns
x32_cd16_non_classical_mono fixed NA interventionYellow -0.3330 0.3102 -1.0735 11 0.3060 ns
x33_cd16_classical_mono fixed NA interventionYellow 0.6321 3.5425 0.1784 11 0.8616 ns
x34_hladr_cd56 fixed NA interventionYellow 0.6282 3.2256 0.1947 11 0.8491 ns
x35_cd16_cd123_cd11c_p_dc fixed NA interventionYellow 0.0082 0.0508 0.1606 11 0.8753 ns
x36_cd16_cd123_cd11c_m_dc fixed NA interventionYellow 0.6105 2.9571 0.2064 11 0.8402 ns
x37_cd56_cd161_cd123_nk_cells fixed NA interventionYellow -4.8544 1.6409 -2.9583 11 0.0130 *
x38_cd16_nk_cells fixed NA interventionYellow 3.3508 0.8126 4.1235 11 0.0017 **
x40_cd14_mdsc_mono fixed NA interventionYellow -10.9242 3.2788 -3.3317 11 0.0067 **
# extract statistically significant cell type 
cells_trt_psig <- cells_trt_coefonly %>%
  filter(cells_trt_coefonly$p.value < 0.05)


print(cells_trt_psig$cell_type)
## [1] "x37_cd56_cd161_cd123_nk_cells" "x38_cd16_nk_cells"            
## [3] "x40_cd14_mdsc_mono"

*3 significant cell types

Plots

sigcells_trt <- cells_trt_psig$cell_type
lineplots
cells_post_long_edited %>% 
  filter(cell_type %in% sigcells_trt) %>%
  ggplot(aes(x = intervention, y = cell_value, color = patient_id)) +
  geom_line(aes(group = patient_id)) +
  labs(x = "",
       y = "",
       title = "") +
  theme_classic() +
  facet_wrap(~ cell_type, scales = "free_y")

boxplots
cells_post_long_edited %>%
  filter(cell_type %in% sigcells_trt) %>%
  ggplot(aes(x = intervention, y = cell_value, fill = intervention)) +
  geom_boxplot(outlier.shape = NA) +
  scale_fill_manual(values = c("Yellow" = "gold",
                               "Red" = "tomato1")) +
  geom_jitter() +
  labs(x = "Intervention", 
       y = "Cell value",
       title = "Significantly different cell types: post- yellow vs. post- red interventions") +
  facet_wrap(~ cell_type, ncol = 3, nrow = 1, scales = "free_y") +
  theme_bw()

  • x38_CD16 NK cells are significantly higher in post- yellow compared to post-red

  • x37_cd56_cd161_cd123_nk_cells and x40_cd14_mdsc_mono significantly lower post-yellow intervention compared to post-red

Yellow juice treatment

# subset data into yellow results
cells_Y_long <- cells_long %>%
  filter(intervention == "Yellow")
# remove x41_cd66b_mdsc_grans because it has too many NAs
cells_Y_long_edited <- cells_Y_long %>%
  filter(cell_type != "x41_cd66b_mdsc_grans")
# treatment effect test lmer model function
cells_trtY_function <- function(cells_Y_long_edited) {
  lmer(cell_value ~ pre_post + (1|patient_id), 
       data = cells_Y_long_edited, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cells_trtY_models <- map(split(cells_Y_long_edited,
                              cells_Y_long_edited$cell_type),
                        cells_trtY_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cells_trtY_results <- map_df(cells_trtY_models,
                            tidy,
                            .id = "cell_type")

# extract fixed coefficients for sequence only
cells_trtY_coefonly <- cells_trtY_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )

Results

# extract statistically significant cytokines 
cells_trtY_psig <- cells_trtY_coefonly %>%
  filter(cells_trtY_coefonly$p.value < 0.05)

print(cells_trtY_psig$cell_type)
## [1] "x05_cd4_cd8_cd8_t_cells"     "x08_cd45ro_cd45ra_em_cd8"   
## [3] "x09_cd45r0_cd45ra_te_cd8"    "x15_cd45ro_cd45ra_te_cd4"   
## [5] "x26_cd27_ig_d_naive_b_cells"

Plots

sigcells_Y <- cells_trtY_psig$cell_type
lineplots
cells_Y_long_edited %>% 
  filter(cell_type %in% sigcells_Y) %>%
  ggplot(aes(x = pre_post, y = cell_value, color = patient_id)) +
  geom_line(aes(group = patient_id)) +
  labs(x = "",
       y = "",
       title = "") +
  theme_classic() +
  facet_wrap(~ cell_type, scales = "free_y")

boxplots
cells_Y_long_edited %>% 
  filter(cell_type %in% sigcells_Y) %>%
  ggplot(aes(x = pre_post, y = cell_value, fill = pre_post)) +
  geom_boxplot(outlier.shape = NA) +
  scale_fill_manual(values = c("pre" = "gray",
                               "post" = "gold")) +
  geom_jitter() +
  labs(x = "Pre vs post", 
       y = "Cell value",
       title = "Significantly different cell types: pre vs post yellow") +
  facet_wrap(~ cell_type, ncol = 3, nrow = 2, scales = "free_y") +
  theme_bw()

*5 significant cell types

Red juice treatment

# subset data into red results
cells_R_long <- cells_long %>%
  filter(intervention == "Red")
# remove x41_cd66b_mdsc_grans because it has too many NAs
cells_R_long_edited <- cells_R_long %>%
  filter(cell_type != "x41_cd66b_mdsc_grans")
# treatment effect test lmer model function
cells_trtR_function <- function(cells_R_long_edited) {
  lmer(cell_value ~ pre_post + (1|patient_id), 
       data = cells_R_long_edited, 
       REML = TRUE)
}

# break data up into subsets based on cell, then apply funtion to each subset
cells_trtR_models <- map(split(cells_R_long_edited,
                               cells_R_long_edited$cell_type),
                         cells_trtR_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cells_trtR_results <- map_df(cells_trtR_models,
                             tidy,
                             .id = "cell_type")

# extract fixed coefficients for sequence only
cells_trtR_coefonly <- cells_trtR_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )

Results

kable(cells_trtR_coefonly, format = "markdown", digits = 3)
cell_type effect group term estimate std.error statistic df p.value p.sig
x01_cd45_cd66b_lymph_dc_mono fixed NA pre_postpost -0.476 0.978 -0.487 11 0.636 ns
x02_cd45_cd66b_grans fixed NA pre_postpost 0.354 0.228 1.556 11 0.148 ns
x03_cd3_cd45_cd3_t_cells fixed NA pre_postpost 0.294 6.161 0.048 11 0.963 ns
x04_tc_rgd_cd3_ab_t_cells fixed NA pre_postpost 0.086 6.144 0.014 11 0.989 ns
x05_cd4_cd8_cd8_t_cells fixed NA pre_postpost 1.983 1.198 1.655 11 0.126 ns
x06_cd45ro_cd45ra_naive_cd8 fixed NA pre_postpost 0.137 0.463 0.296 11 0.773 ns
x07_cd46ro_cd45ra_cm_cd8 fixed NA pre_postpost 0.063 0.049 1.296 11 0.221 ns
x08_cd45ro_cd45ra_em_cd8 fixed NA pre_postpost 1.645 1.099 1.496 11 0.163 ns
x09_cd45r0_cd45ra_te_cd8 fixed NA pre_postpost 0.147 0.191 0.770 11 0.457 ns
x10_cd38_hladr_activated_cd8 fixed NA pre_postpost -0.005 0.014 -0.346 11 0.736 ns
x11_cd4_cd8_cd4_t_cells fixed NA pre_postpost -2.279 5.045 -0.452 11 0.660 ns
x12_cd45ro_cd45ra_naive_cd4 fixed NA pre_postpost -1.932 2.696 -0.717 11 0.488 ns
x13_cd45ro_cd45ra_cm_cd4 fixed NA pre_postpost -0.585 0.924 -0.633 11 0.540 ns
x14_cd45ro_cd45ra fixed NA pre_postpost 0.374 0.255 1.467 11 0.170 ns
x15_cd45ro_cd45ra_te_cd4 fixed NA pre_postpost 1.147 1.105 1.037 22 0.311 ns
x16_cd38_hladr_activated_cd4 fixed NA pre_postpost -0.041 0.031 -1.304 11 0.219 ns
x17_cd25_cd127_tregs fixed NA pre_postpost -0.063 0.070 -0.903 11 0.386 ns
x18_ccr4_cd4_total_ccr4_treg fixed NA pre_postpost -0.056 0.058 -0.967 11 0.354 ns
x19_cd45ra_cd45ro_ccr4_treg_naive fixed NA pre_postpost -0.003 0.004 -0.741 11 0.474 ns
x20_hladr_total_ccr4_treg_activated fixed NA pre_postpost -0.006 0.025 -0.238 11 0.816 ns
x21_cd45ra_cd45ro_ccr4_treg_memory fixed NA pre_postpost -0.041 0.047 -0.885 11 0.395 ns
x22_cxcr3_ccr6_th1 fixed NA pre_postpost 0.631 0.511 1.234 11 0.243 ns
x23_cxcr3_ccr6_th2 fixed NA pre_postpost -4.914 4.239 -1.159 11 0.271 ns
x24_cxcr3_ccr6_th17 fixed NA pre_postpost -0.076 0.433 -0.175 11 0.864 ns
x25_cd19_cd3_b_cells fixed NA pre_postpost 2.985 0.809 3.690 11 0.004 **
x26_cd27_ig_d_naive_b_cells fixed NA pre_postpost 2.506 0.771 3.250 11 0.008 **
x27_cd27_ig_d_memory_b_cells fixed NA pre_postpost 0.163 0.154 1.055 11 0.314 ns
x28_cd27_ig_d_memory_resting_b_cells fixed NA pre_postpost 0.061 0.028 2.152 11 0.054 ns
x30_cd27_cd38_plasmablasts fixed NA pre_postpost -0.018 0.018 -1.004 11 0.337 ns
x31_cd14_monocytes fixed NA pre_postpost -2.571 4.172 -0.616 11 0.550 ns
x32_cd16_non_classical_mono fixed NA pre_postpost -0.594 0.483 -1.231 11 0.244 ns
x33_cd16_classical_mono fixed NA pre_postpost -1.970 3.736 -0.527 11 0.608 ns
x34_hladr_cd56 fixed NA pre_postpost -2.146 3.763 -0.570 11 0.580 ns
x35_cd16_cd123_cd11c_p_dc fixed NA pre_postpost -0.078 0.060 -1.301 11 0.220 ns
x36_cd16_cd123_cd11c_m_dc fixed NA pre_postpost -1.310 3.138 -0.417 11 0.684 ns
x37_cd56_cd161_cd123_nk_cells fixed NA pre_postpost -1.292 1.590 -0.812 11 0.434 ns
x38_cd16_nk_cells fixed NA pre_postpost -0.601 0.521 -1.153 11 0.273 ns
x40_cd14_mdsc_mono fixed NA pre_postpost -1.314 2.014 -0.652 11 0.528 ns
# extract statistically significant cells 
cells_trtR_psig <- cells_trtR_coefonly %>%
  filter(cells_trtR_coefonly$p.value < 0.05)


print(cells_trtR_psig$cell_type)
## [1] "x25_cd19_cd3_b_cells"        "x26_cd27_ig_d_naive_b_cells"

Plots

sigcells_R <- cells_trtR_psig$cell_type
lineplots
cells_R_long_edited %>% 
  filter(cell_type %in% sigcells_R) %>%
  ggplot(aes(x = pre_post, y = cell_value, color = patient_id)) +
  geom_line(aes(group = patient_id)) +
  labs(x = "",
       y = "",
       title = "") +
  theme_classic() +
  facet_wrap(~ cell_type, scales = "free_y")

boxplots
cells_R_long_edited %>% 
  filter(cell_type %in% sigcells_R) %>%
  ggplot(aes(x = pre_post, y = cell_value, fill = pre_post)) +
  geom_boxplot(outlier.shape = NA) +
  scale_fill_manual(values = c("pre" = "gray",
                               "post" = "tomato")) +
  geom_jitter() +
  labs(x = "Pre vs post", 
       y = "Cell value",
       title = "Significantly different cell types: pre vs post Red") +
  facet_wrap(~ cell_type, ncol = 3, nrow = 2, scales = "free_y") +
  theme_bw()

Naive B-cell pop (#26) significantly increase post-Red intervention, and also increase post-Yellow intervention. This could suggest there is a tomato effect.

Log transformed immune cells

resid normality checks

I need to figure out how to make a function that extracts makes a QQplot for each model. But since IL-5 was significant, let’s look at how the residuals look for this cytokine (for every comparison) before log transforming

#qqplot_fx <- function(cytokine){
  #qqnorm(resid(cyto_seq_models$cytokine))
  #qqline(resid(cyto_seq_models$cytokine))
#}

#qqplot_fx("il_5")

#cyto_seq_results$

seq

qqnorm(resid(cells_seq_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_seq_models$x25_cd19_cd3_b_cells))

Doesn’t look bad

intervention

qqnorm(resid(cells_trt_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_trt_models$x25_cd19_cd3_b_cells))

doesn’t look as bad as sequence

red

qqnorm(resid(cells_trtR_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_trtR_models$x25_cd19_cd3_b_cells))

yellow

qqnorm(resid(cells_trtY_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_trtY_models$x25_cd19_cd3_b_cells))

I will log transform the data and perform mixed linear modeling and paired t-tests for every comparison to see how the data compares.

Wrangle

cells_log2_long <- cells_long %>%
  mutate(log2_cell_value = log2(cell_value)) %>%
  filter(log2_cell_value != -Inf) # remove 0s

Sequence effect test

# lmer model function
cells_log2_seq_function <- function(cells_log2_long) {
  lmer(log2_cell_value ~ sequence + (1|patient_id), data = cells_log2_long, REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cells_log2_seq_models <- map(split(cells_log2_long,
                              cells_log2_long$cell_type),
                   cells_log2_seq_function)

# create single data frame of results. apply tidy from broom.mixed package to clean up results
cells_log2_seq_results <- map_df(cells_log2_seq_models,
                            tidy,
                            .id = "cell_type")

cells_log2_seq_results_coef <- cells_log2_seq_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )

Results

kable(cells_log2_seq_results_coef, format = "markdown", digits = 3)
cell_type effect group term estimate std.error statistic df p.value p.sig
x01_cd45_cd66b_lymph_dc_mono fixed NA sequenceY_R 0.002 0.035 0.064 10.000 0.950 ns
x02_cd45_cd66b_grans fixed NA sequenceY_R 0.084 0.876 0.096 10.000 0.926 ns
x03_cd3_cd45_cd3_t_cells fixed NA sequenceY_R 0.226 0.317 0.712 10.000 0.492 ns
x04_tc_rgd_cd3_ab_t_cells fixed NA sequenceY_R 0.213 0.340 0.625 10.000 0.546 ns
x05_cd4_cd8_cd8_t_cells fixed NA sequenceY_R 0.573 0.367 1.562 10.000 0.149 ns
x06_cd45ro_cd45ra_naive_cd8 fixed NA sequenceY_R 0.658 0.858 0.767 10.000 0.461 ns
x07_cd46ro_cd45ra_cm_cd8 fixed NA sequenceY_R 0.500 0.516 0.969 10.000 0.355 ns
x08_cd45ro_cd45ra_em_cd8 fixed NA sequenceY_R 0.891 0.711 1.254 10.000 0.239 ns
x09_cd45r0_cd45ra_te_cd8 fixed NA sequenceY_R -0.186 0.431 -0.432 10.000 0.675 ns
x10_cd38_hladr_activated_cd8 fixed NA sequenceY_R 0.068 0.398 0.172 10.000 0.867 ns
x11_cd4_cd8_cd4_t_cells fixed NA sequenceY_R 0.116 0.507 0.230 10.000 0.823 ns
x12_cd45ro_cd45ra_naive_cd4 fixed NA sequenceY_R 0.489 0.725 0.675 10.000 0.515 ns
x13_cd45ro_cd45ra_cm_cd4 fixed NA sequenceY_R 0.055 0.515 0.108 10.000 0.916 ns
x14_cd45ro_cd45ra fixed NA sequenceY_R 0.383 0.379 1.013 10.000 0.335 ns
x15_cd45ro_cd45ra_te_cd4 fixed NA sequenceY_R 0.964 0.886 1.088 10.000 0.302 ns
x16_cd38_hladr_activated_cd4 fixed NA sequenceY_R 0.141 0.408 0.345 10.000 0.737 ns
x17_cd25_cd127_tregs fixed NA sequenceY_R 0.334 0.449 0.745 10.000 0.473 ns
x18_ccr4_cd4_total_ccr4_treg fixed NA sequenceY_R 0.495 0.448 1.105 10.001 0.295 ns
x19_cd45ra_cd45ro_ccr4_treg_naive fixed NA sequenceY_R 0.727 0.688 1.056 10.000 0.316 ns
x20_hladr_total_ccr4_treg_activated fixed NA sequenceY_R 0.198 0.364 0.545 10.000 0.598 ns
x21_cd45ra_cd45ro_ccr4_treg_memory fixed NA sequenceY_R 0.422 0.442 0.956 10.000 0.362 ns
x22_cxcr3_ccr6_th1 fixed NA sequenceY_R 1.429 1.201 1.190 10.000 0.262 ns
x23_cxcr3_ccr6_th2 fixed NA sequenceY_R 1.660 1.442 1.151 10.000 0.276 ns
x24_cxcr3_ccr6_th17 fixed NA sequenceY_R 1.479 1.223 1.209 10.000 0.254 ns
x25_cd19_cd3_b_cells fixed NA sequenceY_R -0.074 0.456 -0.162 10.000 0.874 ns
x26_cd27_ig_d_naive_b_cells fixed NA sequenceY_R 0.012 0.502 0.023 10.000 0.982 ns
x27_cd27_ig_d_memory_b_cells fixed NA sequenceY_R -0.461 0.551 -0.837 10.000 0.422 ns
x28_cd27_ig_d_memory_resting_b_cells fixed NA sequenceY_R -0.400 0.709 -0.564 10.000 0.585 ns
x30_cd27_cd38_plasmablasts fixed NA sequenceY_R -0.795 0.652 -1.220 9.743 0.251 ns
x31_cd14_monocytes fixed NA sequenceY_R -0.405 0.448 -0.905 10.000 0.387 ns
x32_cd16_non_classical_mono fixed NA sequenceY_R -0.531 0.672 -0.790 10.000 0.448 ns
x33_cd16_classical_mono fixed NA sequenceY_R -0.353 0.447 -0.789 10.000 0.449 ns
x34_hladr_cd56 fixed NA sequenceY_R -0.354 0.442 -0.800 10.000 0.442 ns
x35_cd16_cd123_cd11c_p_dc fixed NA sequenceY_R -0.757 0.625 -1.211 10.000 0.254 ns
x36_cd16_cd123_cd11c_m_dc fixed NA sequenceY_R -0.278 0.457 -0.608 10.000 0.557 ns
x37_cd56_cd161_cd123_nk_cells fixed NA sequenceY_R -0.362 0.644 -0.562 10.000 0.587 ns
x38_cd16_nk_cells fixed NA sequenceY_R 0.213 0.631 0.338 10.000 0.742 ns
x40_cd14_mdsc_mono fixed NA sequenceY_R -0.711 0.789 -0.901 46.000 0.372 ns
x41_cd66b_mdsc_grans fixed NA sequenceY_R -0.441 1.071 -0.412 10.000 0.689 ns
# extract statistically significant cytokines 
cells_log2_seq_psig <- cells_log2_seq_results_coef %>%
  filter(cells_log2_seq_results_coef$p.value < 0.05)

print(cells_log2_seq_psig$cell_type)
## character(0)

*No sequence effects

Resid check

# before log transform
qqnorm(resid(cells_seq_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_seq_models$x25_cd19_cd3_b_cells))

# after log transform
qqnorm(resid(cells_log2_seq_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_log2_seq_models$x25_cd19_cd3_b_cells))

Yellow treatment

# subset data into yellow results
cells_log2_Y_long <- cells_log2_long %>%
  filter(intervention == "Yellow")
# lmer model function
cells_log2_Y_function <- function(cells_log2_Y_long) {
  lmer(log2_cell_value ~ sequence + (1|patient_id), data = cells_log2_Y_long, REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cells_log2_Y_models <- map(split(cells_log2_Y_long,
                              cells_log2_Y_long$cell_type),
                   cells_log2_Y_function)

# create single data frame of results. apply tidy from broom.mixed package to clean up results
cells_log2_Y_results <- map_df(cells_log2_Y_models,
                            tidy,
                            .id = "cell_type")

cells_log2_Y_results_coef <- cells_log2_Y_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )

Results

kable(cells_log2_Y_results_coef, format = "markdown", digits = 3)
cell_type effect group term estimate std.error statistic df p.value p.sig
x01_cd45_cd66b_lymph_dc_mono fixed NA sequenceY_R 0.053 0.049 1.086 10 0.303 ns
x02_cd45_cd66b_grans fixed NA sequenceY_R -0.359 0.994 -0.362 10 0.725 ns
x03_cd3_cd45_cd3_t_cells fixed NA sequenceY_R -0.072 0.320 -0.226 10 0.826 ns
x04_tc_rgd_cd3_ab_t_cells fixed NA sequenceY_R -0.109 0.349 -0.311 10 0.762 ns
x05_cd4_cd8_cd8_t_cells fixed NA sequenceY_R 0.488 0.392 1.245 10 0.242 ns
x06_cd45ro_cd45ra_naive_cd8 fixed NA sequenceY_R 0.001 0.946 0.001 10 1.000 ns
x07_cd46ro_cd45ra_cm_cd8 fixed NA sequenceY_R 0.160 0.533 0.299 10 0.771 ns
x08_cd45ro_cd45ra_em_cd8 fixed NA sequenceY_R 1.258 0.713 1.764 10 0.108 ns
x09_cd45r0_cd45ra_te_cd8 fixed NA sequenceY_R -0.085 0.563 -0.151 10 0.883 ns
x10_cd38_hladr_activated_cd8 fixed NA sequenceY_R 0.366 0.510 0.717 10 0.490 ns
x11_cd4_cd8_cd4_t_cells fixed NA sequenceY_R -0.325 0.518 -0.627 10 0.544 ns
x12_cd45ro_cd45ra_naive_cd4 fixed NA sequenceY_R -0.163 0.776 -0.210 10 0.838 ns
x13_cd45ro_cd45ra_cm_cd4 fixed NA sequenceY_R -0.347 0.528 -0.658 10 0.525 ns
x14_cd45ro_cd45ra fixed NA sequenceY_R 0.469 0.424 1.107 10 0.294 ns
x15_cd45ro_cd45ra_te_cd4 fixed NA sequenceY_R 1.360 0.860 1.582 10 0.145 ns
x16_cd38_hladr_activated_cd4 fixed NA sequenceY_R 0.204 0.515 0.396 10 0.700 ns
x17_cd25_cd127_tregs fixed NA sequenceY_R -0.016 0.504 -0.033 10 0.975 ns
x18_ccr4_cd4_total_ccr4_treg fixed NA sequenceY_R 0.248 0.519 0.478 10 0.643 ns
x19_cd45ra_cd45ro_ccr4_treg_naive fixed NA sequenceY_R 0.540 0.754 0.716 10 0.490 ns
x20_hladr_total_ccr4_treg_activated fixed NA sequenceY_R 0.032 0.450 0.070 10 0.945 ns
x21_cd45ra_cd45ro_ccr4_treg_memory fixed NA sequenceY_R 0.140 0.497 0.281 10 0.784 ns
x22_cxcr3_ccr6_th1 fixed NA sequenceY_R 2.235 1.263 1.769 10 0.107 ns
x23_cxcr3_ccr6_th2 fixed NA sequenceY_R 1.188 1.581 0.752 10 0.470 ns
x24_cxcr3_ccr6_th17 fixed NA sequenceY_R 0.726 1.201 0.604 10 0.559 ns
x25_cd19_cd3_b_cells fixed NA sequenceY_R -0.028 0.425 -0.066 10 0.949 ns
x26_cd27_ig_d_naive_b_cells fixed NA sequenceY_R 0.000 0.471 0.000 10 1.000 ns
x27_cd27_ig_d_memory_b_cells fixed NA sequenceY_R -0.228 0.570 -0.400 10 0.698 ns
x28_cd27_ig_d_memory_resting_b_cells fixed NA sequenceY_R -0.040 0.706 -0.057 10 0.956 ns
x30_cd27_cd38_plasmablasts fixed NA sequenceY_R -0.650 0.737 -0.881 10 0.399 ns
x31_cd14_monocytes fixed NA sequenceY_R 0.164 0.489 0.334 10 0.745 ns
x32_cd16_non_classical_mono fixed NA sequenceY_R 0.448 0.711 0.630 10 0.543 ns
x33_cd16_classical_mono fixed NA sequenceY_R 0.217 0.498 0.436 10 0.672 ns
x34_hladr_cd56 fixed NA sequenceY_R 0.144 0.482 0.298 10 0.772 ns
x35_cd16_cd123_cd11c_p_dc fixed NA sequenceY_R -0.232 0.599 -0.387 10 0.707 ns
x36_cd16_cd123_cd11c_m_dc fixed NA sequenceY_R 0.258 0.507 0.509 10 0.622 ns
x37_cd56_cd161_cd123_nk_cells fixed NA sequenceY_R -0.381 0.724 -0.526 10 0.610 ns
x38_cd16_nk_cells fixed NA sequenceY_R 0.454 0.679 0.670 10 0.518 ns
x40_cd14_mdsc_mono fixed NA sequenceY_R -0.114 0.923 -0.123 10 0.904 ns
# extract statistically significant cytokines 
cells_log2_Y_psig <- cells_log2_Y_results_coef %>%
  filter(cells_log2_Y_results_coef$p.value < 0.05)

print(cells_log2_Y_psig$cell_type)
## character(0)

Now there arent any significant cells for yellow after log transforming

Resid check

# before log transform
qqnorm(resid(cells_trtY_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_trtY_models$x25_cd19_cd3_b_cells))

# after log transform
qqnorm(resid(cells_log2_Y_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_log2_Y_models$x25_cd19_cd3_b_cells))

Red treatment

# subset data into yellow results
cells_log2_R_long <- cells_log2_long %>%
  filter(intervention == "Red")
# lmer model function
cells_log2_R_function <- function(cells_log2_R_long) {
  lmer(log2_cell_value ~ sequence + (1|patient_id), data = cells_log2_R_long, REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cells_log2_R_models <- map(split(cells_log2_R_long,
                              cells_log2_R_long$cell_type),
                   cells_log2_R_function)

# create single data frame of results. apply tidy from broom.mixed package to clean up results
cells_log2_R_results <- map_df(cells_log2_R_models,
                            tidy,
                            .id = "cell_type")

cells_log2_R_results_coef <- cells_log2_R_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )

Results

kable(cells_log2_R_results_coef, format = "markdown", digits = 3)
cell_type effect group term estimate std.error statistic df p.value p.sig
x01_cd45_cd66b_lymph_dc_mono fixed NA sequenceY_R -0.049 0.032 -1.539 10.00 0.155 ns
x02_cd45_cd66b_grans fixed NA sequenceY_R 0.527 0.808 0.652 10.00 0.529 ns
x03_cd3_cd45_cd3_t_cells fixed NA sequenceY_R 0.524 0.345 1.519 10.00 0.160 ns
x04_tc_rgd_cd3_ab_t_cells fixed NA sequenceY_R 0.534 0.365 1.463 10.00 0.174 ns
x05_cd4_cd8_cd8_t_cells fixed NA sequenceY_R 0.658 0.367 1.792 10.00 0.103 ns
x06_cd45ro_cd45ra_naive_cd8 fixed NA sequenceY_R 1.315 0.788 1.670 10.00 0.126 ns
x07_cd46ro_cd45ra_cm_cd8 fixed NA sequenceY_R 0.841 0.543 1.550 10.00 0.152 ns
x08_cd45ro_cd45ra_em_cd8 fixed NA sequenceY_R 0.525 0.743 0.707 10.00 0.496 ns
x09_cd45r0_cd45ra_te_cd8 fixed NA sequenceY_R -0.287 0.318 -0.903 10.00 0.388 ns
x10_cd38_hladr_activated_cd8 fixed NA sequenceY_R -0.229 0.346 -0.662 10.00 0.523 ns
x11_cd4_cd8_cd4_t_cells fixed NA sequenceY_R 0.558 0.527 1.058 10.00 0.315 ns
x12_cd45ro_cd45ra_naive_cd4 fixed NA sequenceY_R 1.141 0.723 1.578 10.00 0.146 ns
x13_cd45ro_cd45ra_cm_cd4 fixed NA sequenceY_R 0.458 0.533 0.860 10.00 0.410 ns
x14_cd45ro_cd45ra fixed NA sequenceY_R 0.298 0.369 0.806 10.00 0.439 ns
x15_cd45ro_cd45ra_te_cd4 fixed NA sequenceY_R 0.568 1.041 0.546 10.00 0.597 ns
x16_cd38_hladr_activated_cd4 fixed NA sequenceY_R 0.078 0.376 0.207 10.00 0.840 ns
x17_cd25_cd127_tregs fixed NA sequenceY_R 0.685 0.418 1.638 10.00 0.132 ns
x18_ccr4_cd4_total_ccr4_treg fixed NA sequenceY_R 0.743 0.410 1.813 10.00 0.100 ns
x19_cd45ra_cd45ro_ccr4_treg_naive fixed NA sequenceY_R 0.914 0.860 1.062 10.00 0.313 ns
x20_hladr_total_ccr4_treg_activated fixed NA sequenceY_R 0.365 0.324 1.126 10.00 0.286 ns
x21_cd45ra_cd45ro_ccr4_treg_memory fixed NA sequenceY_R 0.705 0.409 1.724 10.00 0.115 ns
x22_cxcr3_ccr6_th1 fixed NA sequenceY_R 0.623 1.191 0.523 10.00 0.613 ns
x23_cxcr3_ccr6_th2 fixed NA sequenceY_R 2.133 1.316 1.620 10.00 0.136 ns
x24_cxcr3_ccr6_th17 fixed NA sequenceY_R 2.233 1.285 1.738 10.00 0.113 ns
x25_cd19_cd3_b_cells fixed NA sequenceY_R -0.120 0.506 -0.237 10.00 0.818 ns
x26_cd27_ig_d_naive_b_cells fixed NA sequenceY_R 0.023 0.565 0.041 10.00 0.968 ns
x27_cd27_ig_d_memory_b_cells fixed NA sequenceY_R -0.695 0.554 -1.253 10.00 0.239 ns
x28_cd27_ig_d_memory_resting_b_cells fixed NA sequenceY_R -0.759 0.741 -1.025 10.00 0.330 ns
x30_cd27_cd38_plasmablasts fixed NA sequenceY_R -0.797 0.531 -1.503 8.25 0.170 ns
x31_cd14_monocytes fixed NA sequenceY_R -0.974 0.465 -2.096 10.00 0.063 ns
x32_cd16_non_classical_mono fixed NA sequenceY_R -1.510 0.733 -2.061 10.00 0.066 ns
x33_cd16_classical_mono fixed NA sequenceY_R -0.922 0.458 -2.013 10.00 0.072 ns
x34_hladr_cd56 fixed NA sequenceY_R -0.851 0.459 -1.854 10.00 0.093 ns
x35_cd16_cd123_cd11c_p_dc fixed NA sequenceY_R -1.282 0.684 -1.874 10.00 0.090 ns
x36_cd16_cd123_cd11c_m_dc fixed NA sequenceY_R -0.813 0.463 -1.757 10.00 0.110 ns
x37_cd56_cd161_cd123_nk_cells fixed NA sequenceY_R -0.343 0.671 -0.512 10.00 0.620 ns
x38_cd16_nk_cells fixed NA sequenceY_R -0.028 0.629 -0.044 10.00 0.965 ns
x40_cd14_mdsc_mono fixed NA sequenceY_R -1.309 0.717 -1.825 10.00 0.098 ns
x41_cd66b_mdsc_grans fixed NA sequenceY_R -0.441 1.071 -0.412 10.00 0.689 ns
# extract statistically significant cytokines 
cells_log2_R_psig <- cells_log2_R_results_coef %>%
  filter(cells_log2_R_results_coef$p.value < 0.05)

print(cells_log2_R_psig$cell_type)
## character(0)

Now there arent any significant cells for red after log transforming

Resid check

# before log transform
qqnorm(resid(cells_trtR_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_trtR_models$x25_cd19_cd3_b_cells))

# after log transform
qqnorm(resid(cells_log2_R_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_log2_R_models$x25_cd19_cd3_b_cells))

Looks even worse after log transform

---
title: "Carotenoids and immune data"
author: "Maria Sholola"
date: "2023-11-30"
output: 
  html_document:
    theme: yeti
    toc: true
    toc_float: true
    anchor_sections: true
    code_download: true
    code_folding: show
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
```


# Introduction

Statistical analysis of plasma carotenoids, plasma cytokines and immune cells from randomized cross-over USDA inflammation clinical trial. Subjects consumed both low lycopene tomato (yellow) and high lycopene tomato-soy juices (red) for 4 weeks each.


```{r echo=FALSE, out.width="500px", fig.align='center', fig.cap="Crossover clinical trial design supplementing individuals with obesity 360 mL of a low carotenoid tomato juice or a high lycopene tomato-soy juice daily. Daily serving of low carotenoid tomato juice consisted of ~1.5mg lycopene/day while high lycopene tomato-soy juice intervention consisted of 54 mg lycopene/day in addition to 210 mg total soy isoflavones/day."}

knitr::include_graphics("tomatosoy-clinical-trial-whitebg.png")
```


# Load libraries
```{r, message=FALSE}
library(tidyverse) # data wrangling
library(readxl) # read in excel files
library(janitor) # clean up names in dataset
library(corrr) # finding correlations
library(rstatix) # stats
library(lme4) # mixed linear modeling
library(knitr) # aesthetic table viewing
library(lmerTest) # add pvalue column to lmer models
library(purrr) # create functions
library(broom.mixed) # generate tidy data frames for lmer results
library(MuMIn) # lmer model testing using AICc
library(kableExtra)
library(ggthemes)
library(ggtext)
library(ggpubr)
```


# Read in data

```{r}
# load data
meta_table <- read_excel("CompiledData_Results_Meta.xlsx",
                         sheet = "metadata_corrected_withsequence")

# clean up variable names 
meta_table <- clean_names(meta_table)

str(meta_table)
```


## Wrangle

```{r}
# convert variables that should be factors to factors
meta_table <- meta_table %>%
  mutate(across(.cols = c("patient_id", "period", 
                          "intervention", "intervention_week", 
                          "pre_post", "sex", "sequence"),
                .fns = as.factor))


# some stuff came in as characters but should be numeric
meta_table <- meta_table %>%
  mutate(across(.cols = c("il_2", "il_10", "il_13", "il_4"),
                .fns = as.numeric))

str(meta_table)
```


```{r}
# changing factor levels for pre_post
meta_table$pre_post <- factor(meta_table$pre_post,
                              levels = c("pre", "post"))

levels(meta_table$pre_post)  
```


```{r}
# Calculate total_cis_lyc, total_lyc, and total_carotenoids
meta_table <- meta_table %>%
  rename(n5_cis_lyc = x5_cis_lyc) %>%
  mutate(total_cis_lyc = other_cis_lyc + n5_cis_lyc,
         total_lyc = all_trans_lyc + total_cis_lyc,
         total_carotenoids = lutein + zeaxanthin + b_cryptoxanthin + 
                             a_carotene + b_carotene + total_lyc) 
```


# Carotenoids

## All-trans-lyc levels

```{r}
# line plots for each subject at each timepoint
meta_table %>% 
  ggplot(aes(x = intervention_week, y = all_trans_lyc, color = intervention)) +
  geom_point() + 
  geom_line(aes(group = intervention)) +
  scale_color_manual(values = c("Baseline" = "gray", 
                                           "Yellow" = "gold",
                                           "Red" = "tomato1")) +
  facet_wrap(vars(patient_id), scales = "free_y") + 
  theme_bw() +
  labs(x = "Intervention Week",
       y = "All-trans-lycopene levels (nmol/L)",
       title = "All-trans-lycopene levels in each patient before/after each intervention")
```

## Total cis-lyc levels
```{r}
# line plots for each subject at each timepoint
meta_table %>% 
  ggplot(aes(x = intervention_week, y = total_cis_lyc, color = intervention)) +
  geom_point() + 
  geom_line(aes(group = intervention)) +
  scale_color_manual(values = c("Baseline" = "gray", 
                                           "Yellow" = "gold",
                                           "Red" = "tomato1")) +
  facet_wrap(vars(patient_id)) +
  theme_bw() +
  labs(x = "Intervention Week",
       y = "Total cis-lycopene levels (nmol/L)",
       title = "Total cis-lycopene levels in each patient before/after each intervention")
```

## Total lyc levels

### lineplots 

```{r}
# line plots for each subject at each timepoint
meta_table %>% 
  ggplot(aes(x = intervention_week, y = total_lyc, color = intervention)) +
  geom_point() + 
  geom_line(aes(group = intervention)) +
  scale_color_manual(values = c("Baseline" = "gray", 
                                           "Yellow" = "gold",
                                           "Red" = "tomato1")) +
  facet_wrap(vars(patient_id), scales = "free_y") +
  theme_bw() +
  labs(x = "Intervention Week",
       y = "Total lycopene levels (nmol/L)",
       title = "Total lycopene levels in each patient before/after each intervention")
```


### Boxplots

#### wrangling

```{r}
# create a more specific pre_post_intervention column
meta_table_edited <- meta_table %>%
  unite(col = "pre_post_intervention",
        c("pre_post","intervention"),
        sep = "_",
        remove = FALSE)

# make pre_post_intervention column factors
meta_table_edited$pre_post_intervention <- as.factor(meta_table_edited$pre_post_intervention)

# relevel factor columns
meta_table_edited$pre_post_intervention <- factor(meta_table_edited$pre_post_intervention, levels = c("pre_Yellow", "post_Yellow", "pre_Red", "post_Red"))

meta_table_edited$intervention <- factor(meta_table_edited$intervention,
                                         levels = c("Yellow", "Red"))

```


```{r}
# make legend title
legendtitle_ppintervention <- "Timepoint"


# labels
labs_ppintervention <- c("pre control",
                         "post control",
                         "pre Tomato-Soy",
                         "post Tomato-Soy")
```


#### figure
```{r}
meta_table_edited %>% 
  filter(intervention != "Baseline") %>%
  ggplot(aes(x = intervention, y = total_lyc, fill = pre_post_intervention)) +
  geom_boxplot(outlier.shape = NA) + 
  scale_fill_manual(legendtitle_ppintervention,
                    values = c("pre_Red" = "#FF9966",
                               "post_Red" = "#FF3300",
                               "pre_Yellow" = "#FFFF99",
                               "post_Yellow" = "yellow"),
                    labels = labs_ppintervention) +
  theme_clean() +
  labs(x = "",
       y = "Total lycopene levels (nmol/L)",
       title = "Total lycopene levels before/after juice interventions")
```


```{r, fig.width=12}
meta_table_edited %>% 
  filter(intervention != "Baseline") %>%
  ggplot(aes(x = pre_post_intervention, y = total_lyc, fill = intervention)) +
  geom_boxplot(outlier.shape = NA, show.legend = FALSE) + 
  scale_fill_manual(values = c("Red" = "tomato1",
                               "Yellow" = "yellow1")) +
  scale_x_discrete(labels = labs_ppintervention) +
  theme_clean(base_size = 22, base_family = "sans") +
  labs(x = "",
       y = "Overall plasma lycopene conc. (nmol/L)",
       title = "Total plasma lycopene levels",
       subtitle = "Before and after juice interventions")
```



```{r, fig.width=12}
# publish-ready plot
library(ggpubr)

(total_lyc_levels <- meta_table_edited %>% 
    filter(intervention != "Baseline") %>%
  ggpaired(x = "pre_post", y = "total_lyc", fill = "intervention", line.color = "gray", line.size = 1, facet.by = "intervention", short.panel.labs = FALSE, panel.labs = list(intervention = c("", ""))) +
  scale_fill_manual(values = c("Red" = "tomato1",
                               "Yellow" = "yellow1"),
                    labels = c("Control", "Tomato-Soy"),
                    name = "Intervention") +
  geom_line(aes(group = patient_id), colour = "gray", linewidth = 0.15) +
  theme_clean(base_size = 18, base_family = "sans") +
  labs(x = "",
       y = "nmol/L plasma",
       title = "Concentration of Lycopene",
       subtitle = ""))
```

#### export
```{r, eval=FALSE}
ggsave(filename = "/Users/mariasholola/Documents/GitHub/USDA-Inflammation-Metabolomics/Plots/total-lyc-red-and-yellow-boxplots.svg", plot = total_lyc_levels, width = 12)
```


### Descriptive stats

#### wrangle
```{r}
# convert meta_table_edited to long format for lycopene
meta_table_lyc_long <- meta_table_edited %>%
  pivot_longer(cols = total_lyc,
               names_to = "total_lycopene",
               values_to = "nmol_per_L")
```


#### Avg/std dev

##### tomsoy
```{r}
meta_table_lyc_long %>%
  filter(intervention == "Red") %>%
  group_by(pre_post) %>%
  summarize(mean = mean(nmol_per_L),
            stdev = sd(nmol_per_L))
```

##### yellow
```{r}
meta_table_lyc_long %>%
  filter(intervention == "Yellow") %>%
  group_by(pre_post) %>%
  summarize(mean = mean(nmol_per_L),
            stdev = sd(nmol_per_L))
```

#### Mean fold changes
```{r}
lyc_long_subset <- meta_table_lyc_long %>%
  select(patient_id, nmol_per_L, pre_post_intervention) %>%
  pivot_wider(names_from = pre_post_intervention,
              values_from = nmol_per_L) %>%
  mutate(red_FC = post_Red/pre_Red,
         yellow_FC = post_Yellow/pre_Yellow)

lyc_long_subset %>%
  summarize(mean_red_FC = mean(red_FC),
            mean_yellow_FC = mean(yellow_FC))
```



#### Normality checks

Plot histogram
```{r}
gghistogram(meta_table_lyc_long$nmol_per_L, bins = 40)
```




Shapiro's normality test
```{r}
# shapiro normality test for total lycopene 

meta_table_lyc_long %>%
  group_by(intervention) %>%
  shapiro_test(vars = "nmol_per_L")
```
P val for shapiro test turned out to < 0.05 for both interventions, suggesting data here is not normal. Let's log transform before running paired t-tests.

#### Log transform
```{r}
meta_table_lyc_log2_long <- meta_table_lyc_long %>%
  mutate(log2_nmol_per_L = log2(nmol_per_L))
```

Without transforming, here's a paired t-test
```{r}
compare_means(nmol_per_L ~ pre_post, meta_table_lyc_log2_long, method = "t.test", paired = TRUE, group.by = "intervention")
```

##### normality check

Plot histogram
```{r}
gghistogram(meta_table_lyc_log2_long$log2_nmol_per_L, bins = 40)
```


Shapiro's normality test
```{r}
# shapiro normality test for total lycopene 
meta_table_lyc_log2_long %>%
  group_by(intervention) %>%
  shapiro_test(vars = "log2_nmol_per_L")
```

##### paired t test

```{r}
compare_means(log2_nmol_per_L ~ pre_post, meta_table_lyc_log2_long, method = "t.test", paired = TRUE, group.by = "intervention")
```

Total lycopene levels are significantly increasing only after post-Red intervention

# Cytokines

```{r}
# convert cytokines from wide to long
cytokines_long <- meta_table[-c(25:81)] %>%
  pivot_longer(cols = if_ng:il_4,
               names_to = "cytokines",
               values_to = "cyto_conc_pg_ml")
```

```{r}
#cytokines_long_nona <- na.omit(cytokines_long)
```
*note* 11/30/23: I took this part out since NAs don't matter for lmm


## Mixed linear modeling

After testing several models, the best model turned out to have pre_post as a fixed variable and patient_id as random effect. Carotenoids added no statistically significant effect to the model, suggesting that carotenoids are not contributing to cytokine levels. 
Set REML = FALSE when comparing models.


### Sequence effect test

```{r}
# turn off sci notation
options(scipen = 999)

# sequence effect test lmer model function
cyto_seq_function <- function(cytokines_long) {
  lmer(cyto_conc_pg_ml ~ sequence + (1|patient_id), 
       data = cytokines_long, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cyto_seq_models <- map(split(cytokines_long,
                         cytokines_long$cytokines),
                   cyto_seq_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cyto_seq_results <- map_df(cyto_seq_models,
                          tidy,
                          .id = "cytokines")

# extract fixed coefficients for sequence only
cyto_seq_results_coefonly <- cyto_seq_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )

```


```{r}
# results 
kable(cyto_seq_results_coefonly, format = "markdown", digits = 3)
```


Are there any significant cytokines?
```{r}
# extract statistically significant cytokines 
cytokines_seq_psig <- cyto_seq_results_coefonly %>%
  filter(cyto_seq_results_coefonly$p.value < 0.05)

print(cytokines_seq_psig$cytokines)
```


* No statistically significant cytokines, suggesting there are no sequence effects

### Intervention comparison

```{r}
# filter data for post-intervention only
cytokines_post_long <- cytokines_long %>%
  filter(intervention != "Baseline") %>%
  filter(pre_post == "post")
```


```{r}
# treatment effect test lmer model function
cyto_trt_function <- function(cytokines_post_long) {
  lmer(cyto_conc_pg_ml ~ intervention + (1|patient_id), 
       data = cytokines_post_long, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cyto_trt_models <- map(split(cytokines_post_long,
                         cytokines_post_long$cytokines),
                   cyto_trt_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cyto_trt_results <- map_df(cyto_trt_models,
                          tidy,
                          .id = "cytokines")

# extract fixed coefficients for sequence only
cyto_trt_coefonly <- cyto_trt_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )
```

Results
```{r}
kable(cyto_trt_coefonly, format = "markdown", digits = 3)
```


```{r}
# extract statistically significant cytokines 
cytokines_trt_psig <- cyto_trt_coefonly %>%
  filter(cyto_trt_coefonly$p.value < 0.05)


print(cytokines_trt_psig$cytokines)
```


* There are no cytokines with significantly different levels when comparing post-red vs. post-yellow treatment

### Yellow treatment effects

```{r}
# subset data into yellow results
cytokines_Y <- cytokines_long %>%
  filter(intervention == "Yellow")
```


```{r}
# treatment effect test lmer model function
cyto_trtY_function <- function(cytokines_Y) {
  lmer(cyto_conc_pg_ml ~ pre_post + (1|patient_id), 
       data = cytokines_Y, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cyto_trtY_models <- map(split(cytokines_Y,
                         cytokines_Y$cytokines),
                   cyto_trtY_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cyto_trtY_results <- map_df(cyto_trtY_models,
                          tidy,
                          .id = "cytokines")

# extract fixed coefficients for sequence only
cyto_trtY_coefonly <- cyto_trtY_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )
```

Results
```{r}
kable(cyto_trtY_coefonly, format = "markdown", digits = 3)
```

Significant cytokines
```{r}
# extract statistically significant cytokines 
cytokines_trtY_psig <- cyto_trtY_coefonly %>%
  filter(cyto_trtY_coefonly$p.value < 0.05)


print(cytokines_trtY_psig$cytokines)
```


* No significant cytokines within yellow treatment

### Red treatment effects

```{r}
# subset data into yellow results
cytokines_R <- cytokines_long %>%
  filter(intervention == "Red")
```


```{r}
# treatment effect test lmer model function
cyto_trtR_function <- function(cytokines_R) {
  lmer(cyto_conc_pg_ml ~ pre_post + (1|patient_id), 
       data = cytokines_R, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cyto_trtR_models <- map(split(cytokines_R,
                         cytokines_R$cytokines),
                   cyto_trtR_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cyto_trtR_results <- map_df(cyto_trtR_models,
                          tidy,
                          .id = "cytokines")

# extract fixed coefficients for sequence only
cyto_trtR_coefonly <- cyto_trtR_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )
```

Results
```{r}
kable(cyto_trtR_coefonly, format = "markdown", digits = 3) %>%
  row_spec(11, bold = TRUE)
```

Significant cytokines
```{r}
# extract statistically significant cytokines 
cytokines_trtR_psig <- cyto_trtR_coefonly %>%
  filter(cyto_trtR_coefonly$p.value < 0.05)

print(cytokines_trtR_psig$cytokines)
```

* IL-5 is the only significantly different cytokine within red intervention. Same p value from t-test (= 0.0484)
* IL-5 on average goes down by ~1.8 pg/mL from pre to post in red intervention

#### IL-5 plots

##### lineplots
```{r}
meta_table %>%
  filter(intervention == "Red") %>%
  ggplot(aes(x = intervention_week, y = il_5, 
             color = intervention, group = intervention)) +
  geom_line() +
  theme_classic() +
  labs(x = "Week",
       y = "IL-5 levels (pg/mL)",
       title = "IL-5 changes by subject before and after red intervention",
       color = "Patient ID") +
  facet_wrap(~patient_id,
             scales = "free_y") +
  scale_color_manual(values = c("Red" = "tomato1"))
```

```{r}
meta_table %>% 
  filter(intervention == "Red") %>%
  ggplot(aes(x = pre_post, y = il_5, color = patient_id)) +
  geom_line(aes(group = patient_id)) +
  labs(x = "",
       y = "IL-5 conc (pg/mL)",
       title = "IL-5 levels in each patient before and after red intervention") +
  theme_classic()
```


```{r}
meta_table %>% 
  filter(intervention != "Baseline") %>%
  ggplot(aes(x = pre_post, y = il_5, color = intervention)) +
  geom_line(aes(group = intervention)) +
  scale_color_manual(values = c("Yellow" = "gold",
                                "Red" = "tomato1")) +
  facet_wrap(vars(patient_id), scales = "free_y") + 
  theme_classic() +
  labs(x = "",
       y = "IL-5 conc (pg/mL)",
       title = "IL-5 levels in each patient pre- and post- red and yellow intervention")
```

##### boxplots
```{r}

cytokines_long %>%
  filter(intervention == "Red") %>%
  filter(cytokines == "il_5") %>%
  ggplot(aes(x = pre_post, y = cyto_conc_pg_ml, fill = intervention)) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter() +
  scale_fill_manual(values = c("Red" = "tomato1")) +
  labs(x = "",
       y = "IL- 5 levels (pg/mL)",
       title = "IL-5 levels before and after red intervention") +
  theme_bw()

```

* IL-5 on average goes down by ~1.8 pg/mL from pre to post in red intervention. This is a significant decrease according to the mixed linear model.

# Log-transformed cytokines

I know the data isn't normally distributed. Mixed linear models do not assume normality of the dependant variable, however the residuals should be normally distributed. 

## resid normality checks
I need to figure out how to make a function that extracts makes a QQplot for each model. But since IL-5 was significant, let's look at how the residuals look for this cytokine (for every comparison) before log transforming
```{r}
#qqplot_fx <- function(cytokine){
  #qqnorm(resid(cyto_seq_models$cytokine))
  #qqline(resid(cyto_seq_models$cytokine))
#}

#qqplot_fx("il_5")

#cyto_seq_results$
```

### seq
```{r}
qqnorm(resid(cyto_seq_models$il_5))
qqline(resid(cyto_seq_models$il_5))
```

That doesn't look good.

### intervention

```{r}
qqnorm(resid(cyto_trt_models$il_5))
qqline(resid(cyto_trt_models$il_5))
```

doesn't look as bad as sequence

### red

```{r}
qqnorm(resid(cyto_trtR_models$il_5))
qqline(resid(cyto_trtR_models$il_5))
```

doesn't look that bad for Red

### yellow

```{r}
qqnorm(resid(cyto_trtY_models$il_5))
qqline(resid(cyto_trtY_models$il_5))
```


I will log transform the data and perform mixed linear modeling and paired t-tests for every comparison to see how the data compares. 

Wrangle
```{r}
cytokines_log2conc_long <- cytokines_long %>%
  mutate(log2_cyto_conc_pg_ml = log2(cyto_conc_pg_ml))
```

## Mixed linear modeling

### Sequence effect test

We want to make sure there are no seq effects


```{r}
# sequence effect test lmer model function
cyto_log2_seq_function <- function(cytokines_log2conc_long) {
  lmer(log2_cyto_conc_pg_ml ~ sequence + (1|patient_id), 
       data = cytokines_log2conc_long, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cyto_log2_seq_models <- map(split(cytokines_log2conc_long,
                         cytokines_log2conc_long$cytokines),
                   cyto_log2_seq_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cyto_log2_seq_results <- map_df(cyto_log2_seq_models,
                          tidy,
                          .id = "cytokines")

# extract fixed coefficients for sequence only
cyto_log2_seq_results_coefonly <- cyto_log2_seq_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )

```

#### Resid check

```{r}
# before log transform
qqnorm(resid(cyto_seq_models$il_5))
qqline(resid(cyto_seq_models$il_5))
```


```{r}
# after log transform
qqnorm(resid(cyto_log2_seq_models$il_5))
qqline(resid(cyto_log2_seq_models$il_5))
```
This looks better after log transform.

#### Results
```{r}
# results 
kable(cyto_log2_seq_results_coefonly, format = "markdown", digits = 3)
```


Are there any significant cytokines?
```{r}
# extract statistically significant cytokines 
cytokines_log2_seq_psig <- cyto_log2_seq_results_coefonly %>%
  filter(cyto_log2_seq_results_coefonly$p.value < 0.05)

print(cytokines_log2_seq_psig$cytokines)
```

* No statistically significant cytokines, suggesting there are no sequence effects




### Intervention comparison

```{r}
# filter data for post-intervention only
cytokines_log2conc_post_long <- cytokines_log2conc_long %>%
  filter(intervention != "Baseline") %>%
  filter(pre_post == "post")
```


```{r}
# treatment effect test lmer model function
cyto_log2_trt_function <- function(cytokines_log2conc_post_long) {
  lmer(log2_cyto_conc_pg_ml ~ intervention + (1|patient_id), 
       data = cytokines_log2conc_post_long, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cyto_log2_trt_models <- map(split(cytokines_log2conc_post_long,
                         cytokines_log2conc_post_long$cytokines),
                   cyto_log2_trt_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cyto_log2_trt_results <- map_df(cyto_log2_trt_models,
                          tidy,
                          .id = "cytokines")

# extract fixed coefficients for sequence only
cyto_log2_trt_coefonly <- cyto_log2_trt_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )
```


##### Resid check

```{r}
# before log transform
qqnorm(resid(cyto_trt_models$il_5))
qqline(resid(cyto_trt_models$il_5))
```

```{r}
# log transformed
qqnorm(resid(cyto_log2_trt_models$il_5))
qqline(resid(cyto_log2_trt_models$il_5))
```

Don't know if this helps for intervention comparison

##### Results
```{r}
kable(cyto_log2_trt_coefonly, format = "markdown", digits = 3)
```


```{r}
# extract statistically significant cytokines 
cytokines_log2_trt_psig <- cyto_log2_trt_coefonly %>%
  filter(cyto_log2_trt_coefonly$p.value < 0.05)


print(cytokines_log2_trt_psig$cytokines)
```


* There are no cytokines with significantly different levels when comparing post-red vs. post-yellow treatment

### Yellow treatment effects

```{r}
# subset data into yellow results
cytokines_Y_log2 <- cytokines_log2conc_long %>%
  filter(intervention == "Yellow")
```


```{r}
# treatment effect test lmer model function
cyto_trtY_log2_function <- function(cytokines_Y_log2) {
  lmer(log2_cyto_conc_pg_ml ~ pre_post + (1|patient_id), 
       data = cytokines_Y_log2, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cyto_trtY_log2_models <- map(split(cytokines_Y_log2,
                         cytokines_Y_log2$cytokines),
                   cyto_trtY_log2_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cyto_trtY_log2_results <- map_df(cyto_trtY_log2_models,
                          tidy,
                          .id = "cytokines")

# extract fixed coefficients for sequence only
cyto_trtY_log2_coefonly <- cyto_trtY_log2_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )
```

##### Resid check

```{r}
# before log transform 
qqnorm(resid(cyto_trtY_models$il_5))
qqline(resid(cyto_trtY_models$il_5))
```

```{r}
# after log transform
qqnorm(resid(cyto_trtY_log2_models$il_5))
qqline(resid(cyto_trtY_log2_models$il_5))
```

##### Results
```{r}
kable(cyto_trtY_log2_coefonly, format = "markdown", digits = 3)
```

Significant cytokines
```{r}
# extract statistically significant cytokines 
cytokines_trtY_log2_psig <- cyto_trtY_log2_coefonly %>%
  filter(cyto_trtY_log2_coefonly$p.value < 0.05)


print(cytokines_trtY_log2_psig$cytokines)
```


* No significant cytokines within yellow treatment

### Red treatment effects

```{r}
# subset data into yellow results
cytokines_R_log2 <- cytokines_log2conc_long %>%
  filter(intervention == "Red")
```


```{r}
# treatment effect test lmer model function
cyto_trtR_log2_function <- function(cytokines_R_log2) {
  lmer(log2_cyto_conc_pg_ml ~ pre_post + (1|patient_id), 
       data = cytokines_R_log2, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cyto_trtR_log2_models <- map(split(cytokines_R_log2,
                         cytokines_R_log2$cytokines),
                   cyto_trtR_log2_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cyto_trtR_log2_results <- map_df(cyto_trtR_log2_models,
                          tidy,
                          .id = "cytokines")

# extract fixed coefficients for sequence only
cyto_trtR_log2coefonly <- cyto_trtR_log2_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )
```


##### Resid check

```{r}
# before log transform
qqnorm(resid(cyto_trtR_models$il_5))
qqline(resid(cyto_trtR_models$il_5))
```

```{r}
# log transformed
qqnorm(resid(cyto_trtR_log2_models$il_5))
qqline(resid(cyto_trtR_log2_models$il_5))
```

##### Results
```{r}
kable(cyto_trtR_log2coefonly, format = "markdown", digits = 3) 
```



Significant cytokines
```{r}
# extract statistically significant cytokines 
cytokines_trtR_log2_psig <- cyto_trtR_log2coefonly %>%
  filter(cyto_trtR_log2coefonly$p.value < 0.05)

print(cytokines_trtR_log2_psig$cytokines)
```

*note* 11/30/23: Log transforming changes results a bit. IL-5 is no longer significant (pval = 0.053), and now GM-CSF and IL-12p70 are significant. I've done nonparametric stats on untransformed data, these 2 cytokines along with IL-5 were significant. The question is, should I be doing log transformation for the mixed linear models? Q-Q plot looks better for IL-5 after log transforming...

## Paired t tests

### Red

```{r}
compare_means(log2_cyto_conc_pg_ml ~ pre_post, cytokines_R_log2, method = "t.test", paired = TRUE, group.by = "cytokines")
```

We get the same result for paired t-test on log transformed data

# Immune Cells

## Mixed linear modeling

*note* 11/30/23: if there are any NAs, should I assume below LOD? Or were values not recorded?

```{r}
# convert immune cell data from wide to long
cells_long <- meta_table %>%
  select(1:24, starts_with("x")) %>%
  pivot_longer(cols = x01_cd45_cd66b_lymph_dc_mono:x41_cd66b_mdsc_grans,
               names_to = "cell_type",
               values_to = "cell_value")

```


### Sequence effect test
```{r}
# lmer model function
cells_seq_function <- function(cells_long) {
  lmer(cell_value ~ sequence + (1|patient_id), data = cells_long, REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cells_seq_models <- map(split(cells_long,
                              cells_long$cell_type),
                   cells_seq_function)

# create single data frame of results. apply tidy from broom.mixed package to clean up results
cells_seq_results <- map_df(cells_seq_models,
                            tidy,
                            .id = "cell_type")

cells_seq_results_coef <- cells_seq_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )



```

Results
```{r}
kable(cells_seq_results_coef, format = "markdown", digits = 3)
```

```{r}
# extract statistically significant cytokines 
cells_seq_psig <- cells_seq_results_coef %>%
  filter(cells_seq_results_coef$p.value < 0.05)

print(cells_seq_psig$cell_type)
```

*No sequence effects

### Intervention comparison

```{r}
# filter data for post-intervention only
cells_post_long <- cells_long %>%
  filter(intervention != "Baseline") %>%
  filter(pre_post == "post")

str(cells_post_long)
```

```{r}
# remove x41_cd66b_mdsc_grans because it's causing an error
cells_post_long_edited <- cells_post_long %>%
  filter(cell_type != "x41_cd66b_mdsc_grans")
```



```{r}
# treatment effect test lmer model function
cells_trt_function <- function(cells_post_long_edited) {
  lmer(cell_value ~ intervention + (1|patient_id), 
       data = cells_post_long_edited, 
       REML = TRUE)
}

# break data up into subsets based on cell, then apply funtion to each subset
cells_trt_models <- map(split(cells_post_long_edited,
                              cells_post_long_edited$cell_type),
                        cells_trt_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cells_trt_results <- map_df(cells_trt_models,
                           tidy,
                          .id = "cell_type")

# extract fixed coefficients for sequence only
cells_trt_coefonly <- cells_trt_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )

kable(cells_trt_coefonly, format = "markdown", digits = 4)

# extract statistically significant cell type 
cells_trt_psig <- cells_trt_coefonly %>%
  filter(cells_trt_coefonly$p.value < 0.05)


print(cells_trt_psig$cell_type)

```

*3 significant cell types

#### Plots

```{r}
sigcells_trt <- cells_trt_psig$cell_type
```

##### lineplots
```{r}
cells_post_long_edited %>% 
  filter(cell_type %in% sigcells_trt) %>%
  ggplot(aes(x = intervention, y = cell_value, color = patient_id)) +
  geom_line(aes(group = patient_id)) +
  labs(x = "",
       y = "",
       title = "") +
  theme_classic() +
  facet_wrap(~ cell_type, scales = "free_y")
```



##### boxplots
```{r}
cells_post_long_edited %>%
  filter(cell_type %in% sigcells_trt) %>%
  ggplot(aes(x = intervention, y = cell_value, fill = intervention)) +
  geom_boxplot(outlier.shape = NA) +
  scale_fill_manual(values = c("Yellow" = "gold",
                               "Red" = "tomato1")) +
  geom_jitter() +
  labs(x = "Intervention", 
       y = "Cell value",
       title = "Significantly different cell types: post- yellow vs. post- red interventions") +
  facet_wrap(~ cell_type, ncol = 3, nrow = 1, scales = "free_y") +
  theme_bw()
```


* x38_CD16 NK cells are **significantly higher** in post- yellow compared to post-red

* x37_cd56_cd161_cd123_nk_cells and x40_cd14_mdsc_mono **significantly lower** post-yellow intervention compared to post-red


### Yellow juice treatment

```{r}
# subset data into yellow results
cells_Y_long <- cells_long %>%
  filter(intervention == "Yellow")
```

```{r}
# remove x41_cd66b_mdsc_grans because it has too many NAs
cells_Y_long_edited <- cells_Y_long %>%
  filter(cell_type != "x41_cd66b_mdsc_grans")
```


```{r}
# treatment effect test lmer model function
cells_trtY_function <- function(cells_Y_long_edited) {
  lmer(cell_value ~ pre_post + (1|patient_id), 
       data = cells_Y_long_edited, 
       REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cells_trtY_models <- map(split(cells_Y_long_edited,
                              cells_Y_long_edited$cell_type),
                        cells_trtY_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cells_trtY_results <- map_df(cells_trtY_models,
                            tidy,
                            .id = "cell_type")

# extract fixed coefficients for sequence only
cells_trtY_coefonly <- cells_trtY_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )

```

Results
```{r}
# extract statistically significant cytokines 
cells_trtY_psig <- cells_trtY_coefonly %>%
  filter(cells_trtY_coefonly$p.value < 0.05)

print(cells_trtY_psig$cell_type)
```

#### Plots

```{r}
sigcells_Y <- cells_trtY_psig$cell_type
```

##### lineplots
```{r}
cells_Y_long_edited %>% 
  filter(cell_type %in% sigcells_Y) %>%
  ggplot(aes(x = pre_post, y = cell_value, color = patient_id)) +
  geom_line(aes(group = patient_id)) +
  labs(x = "",
       y = "",
       title = "") +
  theme_classic() +
  facet_wrap(~ cell_type, scales = "free_y")
```



##### boxplots
```{r}
cells_Y_long_edited %>% 
  filter(cell_type %in% sigcells_Y) %>%
  ggplot(aes(x = pre_post, y = cell_value, fill = pre_post)) +
  geom_boxplot(outlier.shape = NA) +
  scale_fill_manual(values = c("pre" = "gray",
                               "post" = "gold")) +
  geom_jitter() +
  labs(x = "Pre vs post", 
       y = "Cell value",
       title = "Significantly different cell types: pre vs post yellow") +
  facet_wrap(~ cell_type, ncol = 3, nrow = 2, scales = "free_y") +
  theme_bw()
```

*5 significant cell types

### Red juice treatment

```{r}
# subset data into red results
cells_R_long <- cells_long %>%
  filter(intervention == "Red")
```

```{r}
# remove x41_cd66b_mdsc_grans because it has too many NAs
cells_R_long_edited <- cells_R_long %>%
  filter(cell_type != "x41_cd66b_mdsc_grans")
```

```{r}
# treatment effect test lmer model function
cells_trtR_function <- function(cells_R_long_edited) {
  lmer(cell_value ~ pre_post + (1|patient_id), 
       data = cells_R_long_edited, 
       REML = TRUE)
}

# break data up into subsets based on cell, then apply funtion to each subset
cells_trtR_models <- map(split(cells_R_long_edited,
                               cells_R_long_edited$cell_type),
                         cells_trtR_function)

# create single data frame of results by applying the lmer function to each element in list. apply tidy from broom.mixed package to clean up results
cells_trtR_results <- map_df(cells_trtR_models,
                             tidy,
                             .id = "cell_type")

# extract fixed coefficients for sequence only
cells_trtR_coefonly <- cells_trtR_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )
```

Results

```{r}
kable(cells_trtR_coefonly, format = "markdown", digits = 3)

# extract statistically significant cells 
cells_trtR_psig <- cells_trtR_coefonly %>%
  filter(cells_trtR_coefonly$p.value < 0.05)


print(cells_trtR_psig$cell_type)
```


#### Plots

```{r}
sigcells_R <- cells_trtR_psig$cell_type
```

##### lineplots
```{r}
cells_R_long_edited %>% 
  filter(cell_type %in% sigcells_R) %>%
  ggplot(aes(x = pre_post, y = cell_value, color = patient_id)) +
  geom_line(aes(group = patient_id)) +
  labs(x = "",
       y = "",
       title = "") +
  theme_classic() +
  facet_wrap(~ cell_type, scales = "free_y")
```


##### boxplots
```{r}
cells_R_long_edited %>% 
  filter(cell_type %in% sigcells_R) %>%
  ggplot(aes(x = pre_post, y = cell_value, fill = pre_post)) +
  geom_boxplot(outlier.shape = NA) +
  scale_fill_manual(values = c("pre" = "gray",
                               "post" = "tomato")) +
  geom_jitter() +
  labs(x = "Pre vs post", 
       y = "Cell value",
       title = "Significantly different cell types: pre vs post Red") +
  facet_wrap(~ cell_type, ncol = 3, nrow = 2, scales = "free_y") +
  theme_bw()
```


Naive B-cell pop (#26) significantly increase post-Red intervention, and also increase post-Yellow intervention. This could suggest there is a tomato effect. 

# Log transformed immune cells

## resid normality checks
I need to figure out how to make a function that extracts makes a QQplot for each model. But since IL-5 was significant, let's look at how the residuals look for this cytokine (for every comparison) before log transforming
```{r}
#qqplot_fx <- function(cytokine){
  #qqnorm(resid(cyto_seq_models$cytokine))
  #qqline(resid(cyto_seq_models$cytokine))
#}

#qqplot_fx("il_5")

#cyto_seq_results$
```

### seq
```{r}
qqnorm(resid(cells_seq_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_seq_models$x25_cd19_cd3_b_cells))
```

Doesn't look bad

### intervention

```{r}
qqnorm(resid(cells_trt_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_trt_models$x25_cd19_cd3_b_cells))
```

doesn't look as bad as sequence

### red

```{r}
qqnorm(resid(cells_trtR_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_trtR_models$x25_cd19_cd3_b_cells))
```


### yellow

```{r}
qqnorm(resid(cells_trtY_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_trtY_models$x25_cd19_cd3_b_cells))
```

I will log transform the data and perform mixed linear modeling and paired t-tests for every comparison to see how the data compares. 

Wrangle
```{r}
cells_log2_long <- cells_long %>%
  mutate(log2_cell_value = log2(cell_value)) %>%
  filter(log2_cell_value != -Inf) # remove 0s
```

## Sequence effect test
```{r}
# lmer model function
cells_log2_seq_function <- function(cells_log2_long) {
  lmer(log2_cell_value ~ sequence + (1|patient_id), data = cells_log2_long, REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cells_log2_seq_models <- map(split(cells_log2_long,
                              cells_log2_long$cell_type),
                   cells_log2_seq_function)

# create single data frame of results. apply tidy from broom.mixed package to clean up results
cells_log2_seq_results <- map_df(cells_log2_seq_models,
                            tidy,
                            .id = "cell_type")

cells_log2_seq_results_coef <- cells_log2_seq_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )



```

### Results
```{r}
kable(cells_log2_seq_results_coef, format = "markdown", digits = 3)
```

```{r}
# extract statistically significant cytokines 
cells_log2_seq_psig <- cells_log2_seq_results_coef %>%
  filter(cells_log2_seq_results_coef$p.value < 0.05)

print(cells_log2_seq_psig$cell_type)
```

*No sequence effects

### Resid check

```{r}
# before log transform
qqnorm(resid(cells_seq_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_seq_models$x25_cd19_cd3_b_cells))
```

```{r}
# after log transform
qqnorm(resid(cells_log2_seq_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_log2_seq_models$x25_cd19_cd3_b_cells))
```


## Yellow treatment

```{r}
# subset data into yellow results
cells_log2_Y_long <- cells_log2_long %>%
  filter(intervention == "Yellow")
```


```{r}
# lmer model function
cells_log2_Y_function <- function(cells_log2_Y_long) {
  lmer(log2_cell_value ~ sequence + (1|patient_id), data = cells_log2_Y_long, REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cells_log2_Y_models <- map(split(cells_log2_Y_long,
                              cells_log2_Y_long$cell_type),
                   cells_log2_Y_function)

# create single data frame of results. apply tidy from broom.mixed package to clean up results
cells_log2_Y_results <- map_df(cells_log2_Y_models,
                            tidy,
                            .id = "cell_type")

cells_log2_Y_results_coef <- cells_log2_Y_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )



```

### Results
```{r}
kable(cells_log2_Y_results_coef, format = "markdown", digits = 3)
```

```{r}
# extract statistically significant cytokines 
cells_log2_Y_psig <- cells_log2_Y_results_coef %>%
  filter(cells_log2_Y_results_coef$p.value < 0.05)

print(cells_log2_Y_psig$cell_type)
```

Now there arent any significant cells for yellow after log transforming

### Resid check

```{r}
# before log transform
qqnorm(resid(cells_trtY_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_trtY_models$x25_cd19_cd3_b_cells))
```

```{r}
# after log transform
qqnorm(resid(cells_log2_Y_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_log2_Y_models$x25_cd19_cd3_b_cells))
```


## Red treatment

```{r}
# subset data into yellow results
cells_log2_R_long <- cells_log2_long %>%
  filter(intervention == "Red")
```


```{r}
# lmer model function
cells_log2_R_function <- function(cells_log2_R_long) {
  lmer(log2_cell_value ~ sequence + (1|patient_id), data = cells_log2_R_long, REML = TRUE)
}

# break data up into subsets based on cytokine, then apply funtion to each subset
cells_log2_R_models <- map(split(cells_log2_R_long,
                              cells_log2_R_long$cell_type),
                   cells_log2_R_function)

# create single data frame of results. apply tidy from broom.mixed package to clean up results
cells_log2_R_results <- map_df(cells_log2_R_models,
                            tidy,
                            .id = "cell_type")

cells_log2_R_results_coef <- cells_log2_R_results %>%
  filter(effect == "fixed") %>%
  filter(term != "(Intercept)") %>%
  add_significance(p.col = "p.value",
                   output.col = "p.sig",
                   cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
                   symbols = c("****", "***", "**", "*", "ns")
                   )



```

### Results
```{r}
kable(cells_log2_R_results_coef, format = "markdown", digits = 3)
```

```{r}
# extract statistically significant cytokines 
cells_log2_R_psig <- cells_log2_R_results_coef %>%
  filter(cells_log2_R_results_coef$p.value < 0.05)

print(cells_log2_R_psig$cell_type)
```

Now there arent any significant cells for red after log transforming

### Resid check

```{r}
# before log transform
qqnorm(resid(cells_trtR_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_trtR_models$x25_cd19_cd3_b_cells))
```

```{r}
# after log transform
qqnorm(resid(cells_log2_R_models$x25_cd19_cd3_b_cells))
qqline(resid(cells_log2_R_models$x25_cd19_cd3_b_cells))
```

Looks even worse after log transform
